Schoen et al. 2023 - Code Analysis

General

In this notebook the code for the publication “Schoen et al. 2023, Christensenella minuta boosts gut microbial biomass and voluntary physical activity in mice” for all analyses is provided.

Packages

if (!require("data.table")) install_packages("data.table")

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library(data.table)
if (!require("tidyverse")) install_packages("tidyverse")

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library(tidyverse)
if (!require("ggplot2")) install_packages("ggplot2")
library(ggplot2)
if (!require("ggthemes")) install_packages("ggthemes")

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library(ggthemes)
if (!require("scales")) install_packages("scales")

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library(scales)
if (!require("ggforce")) install_packages("ggforce")

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library(ggforce)
if (!require("ggpubr")) install_packages("ggpubr")

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library(ggpubr)
if (!require('lubridate')) install_packages('lubridate')

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library(lubridate)
if (!require('car')) install_packages('car')

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library(car)
if (!require('Rmisc')) install_packages('Rmisc')

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library(Rmisc)
if (!require('bestNormalize')) install_packages('bestNormalize')

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library(bestNormalize)
if (!require('PerformanceAnalytics')) install_packages('PerformanceAnalytics')

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library(PerformanceAnalytics)
if (!require('lmerTest')) install_packages('lmerTest')

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library(lmerTest)
if (!require('reshape2')) install_packages('reshape2')

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library(reshape2)
if (!require('emmeans')) install_packages('emmeans')

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library(emmeans)
if (!require('Maaslin2')) install_packages('Maaslin2')

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library(Maaslin2)
if (!require("effects")) install.packages("effects")

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See ?effectsTheme for details.

library(effects)
if (!require("vegan")) install.packages("vegan")

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library(vegan)
if (!require("patchwork")) install.packages("patchwork")

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library(patchwork)
if (!require("ggmap")) install_packages("ggmap")

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ℹ Google's Terms of Service: <https://mapsplatform.google.com>
ℹ Please cite ggmap if you use it! Use `citation("ggmap")` for details.

library(ggmap)
if (!require("maps")) install_packages("maps")

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library(maps)
if (!require("mapdata")) install_packages("mapdata")

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library(mapdata)
if (!require("partR2")) remotes::install_github("mastoffel/partR2") 

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library(partR2)
if (!require("rsq")) install_packages("rsq")

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library(rsq)
if (!require("cowplot")) install_packages("cowplot")

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library(cowplot)
if (!require("ggmap")) install_packages("ggmap")
library(ggmap)
if (!require("maps")) install_packages("maps")
library(maps)
if (!require("mapdata")) install_packages("mapdata")
library(mapdata)
if (!require("patchwork")) install_packages("patchwork")
library(patchwork)
if (!require("ggh4x")) install_packages("ggh4x")

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library(ggh4x)
if (!require("ggbeeswarm")) install_packages("ggbeeswarm")

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library(ggbeeswarm)

Functions

backup_options <- options() #only run when restarting the session to save the default options
options(backup_options)
shape_Sex <- c(16, 4)
color_Category <- c("darkgoldenrod1", "darkblue")
color_HL <- c("sienna1", "deepskyblue")
color_distict <- c("#000000","#004949","#009292","#ff6db6","#ffb6db",
 "#490092","#006ddb","#b66dff","#6db6ff","#b6dbff",
 "#920000","#924900","#db6d00","#24ff24","#ffff6d")

properColnames=function(x){
   gsub(" ", "_", 
   gsub("-", "_", 
   gsub(":", "_", 
   gsub("\\+", "_", 
   gsub("\\.", "_",
   gsub("\\(", "", 
   gsub("\\)", "", 
   gsub("^\\d", "", names(x)))))))))
}

properNames = function(x){
   gsub(" ", "_", 
   gsub("-", "_", 
   gsub(":", "_", 
   gsub("\\+", "_",
   gsub("\\.", "_", 
   gsub("\\(", "", 
   gsub("\\)", "", 
   gsub("^\\d", "", x))))))))
}

Set working directory

setwd(dir = "XXXX")

Data

# Christensenellacae global human studies
Figure_globe_data <- fread("./data/FigureS1/map_data.txt") %>% dplyr::arrange(-Sample_size_of_cohort)

# metadata including murine data, body composition measurements, weekly weight measurement, spilled food and feces in respirometry cages, bomb calorimetry mesurements, and qPCR mesurements
metadata <- fread("./data/Metadata/Mouse_metadata.txt") 
Metadata_exclude_id <- c(6296,6305,6374,6372, #sick
                         6350,6426,6451, 6455) #died
metadata <- metadata %>% dplyr::filter(!str_detect(MouseID, paste(Metadata_exclude_id, collapse="|")))
#Promethion
Promethion <- fread("./data/Promethion/Promethion_data_raw.txt")
Circadian <- fread("./data/Promethion/Promethion_data_circadian.txt")

# Metabolomics
## LCMS
### untargeted
LCMS_xcms_output <- fread("./data/Metabolomics/LCMS_serum_untargeted_XCMS.diffreport.MultiClass.tsv")
### targeted
LCMS_serum <- fread("./data/Metabolomics/LCMS_trargeted_serum_area.csv") 
## GC-MS: SCFA
GCMS_SCFAs <- fread("./data/Metabolomics/GCMS_SCFAs.txt") 

# Metagenomics
## qPCR
qPCR_data <- fread("./data/Metagenomics/qPCR_data.txt") 
## Shotgun Sequencing
Metagenomics_metadata_CC <- fread("./data/Metagenomics/Metagenomics_metadata_CC.txt") 

Metagenomics_exclude_id <- c("6296", "6305", "6350", "6374", "6372", "6426", "6451", "6455" # sick anmals
                                , "6434", "6420") #low DNA extraction yield --> low qPCR signal --> normalization not possible
Bracken_otu_table_relFreq <- fread("./data/Metagenomics/Bracken_otu_table_relFreq.txt") # with Donor
Bracken_otu_table_reads <- fread("./data/Metagenomics/Bracken_otu_table_reads.txt") # without Donor

General modification of data

Promethion time series recordings

start_date <- as.Date("2020-05-01") #defining common starting date for all recordings

Promethion.2 <- Promethion %>%
  dplyr::filter(!str_detect(MouseID, paste(Metadata_exclude_id, collapse="|"))) %>%
  dplyr::mutate(Date = as.POSIXct(trunc(as.POSIXct(DateTime), units = "hours"))) %>%
  dplyr::group_by(MouseID) %>%
  dplyr::mutate(difference_start_date = round(as.numeric(difftime(as.Date(min(DateTime), format = "%Y%m%d"), start_date, units = "day"))), 
                Date_common = Date - lubridate::days(difference_start_date)) %>% # create a date-column with a common starting date
  dplyr::ungroup() %>%
  dplyr::left_join(metadata, by = c("MouseID", "Batch", "Promethion_Position"))

Promethion circadian recordings

Circadian.2 <- Circadian %>%
  dplyr::filter(!str_detect(MouseID, paste(Metadata_exclude_id, collapse="|"))) %>%
  dplyr::mutate(StartDate = as.POSIXct(StartDate)) %>%
  dplyr::group_by(MouseID) %>%
  dplyr::mutate(difference_start_date = round(as.numeric(difftime(as.Date(min(StartDate), format = "%Y%m%d"), start_date, units = "day"))), 
                StartDate_common = StartDate - lubridate::days(difference_start_date)) %>% # create a date-column with a common starting date
  dplyr::ungroup() %>% 
  dplyr::filter(StartDate_common != "2020-05-04", 
                StartDate_common != "2020-05-01") # exclude acclimatization and take down

Analysis

Sorted by paragraphs of results ## Figure S1 - Global map of studies associating the gut bacterial family Christensenellaceae with healthy body mass index and/or metabolic health in human gut microbiome studies.

FigureS1 <- ggplot() + 
  geom_polygon(data = map_data('world'), 
               aes(x=long, y = lat, group = group),
               fill='gray20', color='white', size=0.1) +
  geom_point(data=Figure_globe_data, aes(x=Long, y=Lat, size=Sample_size_of_cohort, fill = Association),
            alpha=0.6, shape =21, color = "white") +
  scale_fill_manual(values = c("#CC79A7", "#009E73")) +
  coord_cartesian(xlim = c(-160, 180),
                  ylim = c(-50, 80)) +
  scale_size_continuous('Sample\nsize',
                        range=c(1,12),
                        breaks=c(100, 500, 2000, 5000)) +
  labs(x='Longitude', y='Latitude') +
  theme_classic(base_size = 10) +
  theme(
    panel.grid = element_blank(),
    panel.background = element_rect(fill=alpha('lightsteelblue', 1))
  ) +
  guides(fill = guide_legend(override.aes = list(size = 4, alpha = 1)), size = guide_legend(override.aes = list(shape = 21, color = "black")))

FigureS1

ggsave("./FigureS1.pdf", plot = FigureS1, device = cairo_pdf,  
  width = 16.5,
  height = 7.5,
  units = "cm")

Mice with live C. minuta show lower feed efficiency

C. minuta abundance

metadata_Cmin <- metadata %>% 
  dplyr::filter(MouseID != 6434, MouseID != "6420", # too low DNA yield during DNA extraction --> technical issue
                MouseID != 6477) %>%
  dplyr::left_join(qPCR_data, by ="MouseID")# extreme outlier according to residuals -> excluded for normality reasons 

Fig1_qPCR_Cmin<-metadata_Cmin %>% 
  dplyr::mutate(Cmin_per_gCC_1e7 = qPCR_Cmin_per_gCC / 10000000)  %>% 
  ggplot(aes(x = Category, y = Cmin_per_gCC_1e7, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = expression(paste("C. minuta (GE/g) x",e^7))) +
  theme(axis.text.x = element_text(face = "italic")) 
Fig1_qPCR_Cmin

set.seed(569)
metadata_Cmin$qPCR_Cmin_per_gCC_norm <- predict(bestNormalize(metadata_Cmin$qPCR_Cmin_per_gCC))

options(contrasts=c("contr.sum", "contr.poly"))
model_Cmin<-lmer(qPCR_Cmin_per_gCC_norm ~ Category + qPCR_Round + (Category | Batch), data = metadata_Cmin)
lmerTest::step(model_Cmin)

Backward reduced random-effect table:

                               Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)    
<none>                                       7 -173.33 360.67                         
Category in (Category | Batch)          0    5 -191.78 393.56 36.888  2  9.771e-09 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

           Eliminated Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
Category            0 9.3749  9.3749     1 11.008  28.863 0.0002254 ***
qPCR_Round          0 9.8730  9.8730     1 11.160  30.396 0.0001733 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
qPCR_Cmin_per_gCC_norm ~ Category + qPCR_Round + (Category | Batch)

Testing model assumptions (normality and heteroscedasticity of residuals)

options(contrasts=c("contr.sum", "contr.poly"))
model_Cmin.2<-lmer(qPCR_Cmin_per_gCC_norm ~ Category + qPCR_Round + (Category | Batch), data = metadata_Cmin)

shapiro.test(residuals(model_Cmin.2)) # test normality of residuals

    Shapiro-Wilk normality test

data:  residuals(model_Cmin.2)
W = 0.98985, p-value = 0.2265

anova(lm(residuals(model_Cmin.2) ~ fitted(model_Cmin.2))) # test for heteroscedasticity 

Analysis of Variance Table

Response: residuals(model_Cmin.2)
                      Df Sum Sq Mean Sq F value Pr(>F)
fitted(model_Cmin.2)   1  0.133 0.13338  0.4445 0.5058
Residuals            179 53.716 0.30009               

plot(model_Cmin.2)

options(contrasts=c("contr.sum", "contr.poly"))
car::Anova(model_Cmin.2, type = "II", test.statistic ="F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: qPCR_Cmin_per_gCC_norm
                F Df Df.res    Pr(>F)    
Category   28.857  1 10.999 0.0002262 ***
qPCR_Round 25.133  1 10.861 0.0004098 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Calculation of means and SD with raw values

model_Cmin.eff<-lmer(qPCR_Cmin_per_gCC ~ Category + qPCR_Round + (Category | Batch), data = metadata_Cmin)

Warning: Model may not have converged with 1 eigenvalue close to zero: 1.5e-11

effects::effect("Category", model_Cmin.eff, se =TRUE) %>% as.data.frame()

examination of C. minuta in Donor sample

Bracken_otu_table_relFreq %>% 
  dplyr::select(-contains(Metagenomics_exclude_id), -taxonomy) %>% 
  dplyr::filter(OTU_ID == "s__Christensenella minuta") %>%
  dplyr::rename(rowname = OTU_ID) %>% 
  tidyr::gather(var, value, -rowname) %>% 
  tidyr::spread(rowname, value) %>% 
  dplyr::rename(sampleid = var, s__Cmin_rel = "s__Christensenella minuta") %>% 
  dplyr::left_join(Metagenomics_metadata_CC, by ="sampleid") %>%
  dplyr::left_join(metadata, by ="MouseID") %>%
  dplyr::mutate(Category = ifelse(grepl("Donor", sampleid), "Donor", Category)) %>% 
  ggplot(aes(x = Category, y = s__Cmin_rel, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = c("black", color_Category), label = c("Donor","Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "relative C.minuta") +
  theme(axis.text.x = element_text(face = "italic"))

Murine weight and body composition

Body weight

metadata_weight <- metadata %>% 
  dplyr::mutate(Category_Sex = paste(Category, Sex, sep="_")) %>%
  dplyr::select(MouseID, Batch, Sex, Category, Category_Sex, Litter, T0_weight, T1_weight, T2_weight, T3_weight, Promethion_weight) %>%
  dplyr::rename("7" = "T1_weight", "14" = "T2_weight", "21" = "T3_weight", "28" = "Promethion_weight") %>%
  tidyr::gather(Day, weight, "7":"28")

metadata_weight_withT0<- metadata %>%
  dplyr::mutate(Category_Sex = paste(Category, Sex, sep="_")) %>%
  dplyr::select(MouseID, Batch, Sex, Category, Category_Sex, Litter, T0_weight, T1_weight, T2_weight, T3_weight, Promethion_weight) %>%
  dplyr::rename("0" = "T0_weight", "7" = "T1_weight", "14" = "T2_weight", "21" = "T3_weight", "28" = "Promethion_weight") %>%
  tidyr::gather(Day, weight, "0":"28") %>%
  dplyr::mutate(Day = as.numeric(Day))

metadata_weight_withT0.2 <- na.omit(metadata_weight_withT0)

metadata_weight_withT0_summary <- Rmisc::summarySE(metadata_weight_withT0.2, measurevar =  "weight",
                            groupvar = c("Category", "Day", "Sex", "Category_Sex"), conf.interval = 0.95)

FigS2_weight <- ggplot(metadata_weight_withT0_summary, aes(x=Day, y=weight, group=Category_Sex, color=Category)) + 
  geom_line(alpha = 0.7) +
  geom_errorbar(aes(ymin=weight-ci, ymax=weight+ci),
                 position=position_dodge(0.05), alpha = 0.7, linetype = "dashed") +
  geom_point(aes(shape = Sex), size = 3, alpha =1) +
  scale_shape_manual(values=shape_Sex) +
  labs(y = "Weight (g)", x = "Days") +
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  scale_x_continuous(breaks = c(0, 7, 14, 21, 25, 28)) +
  theme_classic(base_size = 10) 
FigS2_weight

statistical analysis

set.seed(673)
metadata_weight$weight_norm<- predict(bestNormalize(metadata_weight$weight))
metadata_weight$T0_weight_norm<- predict(bestNormalize(metadata_weight$T0_weight))

metadata_weight$Day_factor <- as.factor(metadata_weight$Day)
metadata_weight$MouseID_factor <- as.factor(metadata_weight$MouseID)

options(contrasts=c("contr.sum", "contr.poly"))
model_weight<- lmerTest::lmer(weight_norm ~ Category*Sex*Day_factor + T0_weight_norm + (1|Batch/Sex) + (1|MouseID_factor/Batch/Sex), data = metadata_weight)

Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 3 negative eigenvaluesWarning: Model failed to converge with 2 negative eigenvalues: -4.9e-04 -4.9e-04

lmerTest::step(model_weight, keep = "Category")

Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model is nearly unidentifiable: large eigenvalue ratio
 - Rescale variables?Warning: Model failed to converge with 1 negative eigenvalue: -2.3e-05Warning: Model is nearly unidentifiable: large eigenvalue ratio
 - Rescale variables?Warning: Model failed to converge with 1 negative eigenvalue: -2.3e-05Warning: Model is nearly unidentifiable: large eigenvalue ratio
 - Rescale variables?Warning: Model failed to converge with 1 negative eigenvalue: -2.3e-05Warning: Model is nearly unidentifiable: large eigenvalue ratio
 - Rescale variables?Warning: Model failed to converge with 1 negative eigenvalue: -2.3e-05Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -8.0e-05Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -8.0e-05Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -8.0e-05Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -2.7e-05

Backward reduced random-effect table:

                                 Eliminated npar   logLik    AIC    LRT Df Pr(>Chisq)    
<none>                                        23  -41.739 129.48                         
(1 | Sex:(Batch:MouseID_factor))          1   22  -41.739 127.48   0.00  1          1    
(1 | Sex:Batch)                           2   21  -41.739 125.48   0.00  1          1    
(1 | MouseID_factor)                      3   20  -41.739 123.48   0.00  1          1    
(1 | Batch)                               0   19  -69.436 176.87  55.39  1   9.86e-14 ***
(1 | Batch:MouseID_factor)                0   19 -256.417 550.83 429.36  1  < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                        Eliminated  Sum Sq Mean Sq NumDF  DenDF  F value    Pr(>F)    
Category:Sex:Day_factor          1  0.1798  0.0599     3 534.43   1.9408    0.1220    
Category:Day_factor              2  0.0189  0.0063     3 537.44   0.2033    0.8941    
Category:Sex                     3  0.0089  0.0089     1 169.32   0.2871    0.5928    
Category                         0  0.0453  0.0453     1 170.33   1.4653    0.2278    
T0_weight_norm                   0 10.2728 10.2728     1 180.04 332.3250 < 2.2e-16 ***
Sex:Day_factor                   0  1.1262  0.3754     3 540.45  12.1439 1.059e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
weight_norm ~ Category + Sex + Day_factor + T0_weight_norm + (1 | Batch) + (1 | Batch:MouseID_factor) + Sex:Day_factor

–> not significant for catgeoty or interactions of category wirth other variables –> no treatment effect on weight

Adiposity gain

metadata_gain <- metadata %>%
  dplyr::mutate(T0_fatperc = T0_fatmass/T0_weight*100, 
                T4_fatperc = T4_fatmass/T4_weight*100, 
                adiposity_gain = T4_fatperc - T0_fatperc) %>%
  dplyr::select(MouseID, Sex, Batch, Category, Litter, Promethion_Cabinet, T0_fatperc, T4_fatperc, T0_weight, adiposity_gain) 

Fig1_adiposity <- metadata_gain %>% 
  ggplot(aes(x = Category, y = adiposity_gain, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Adiposity gain (%)") +
  theme(axis.text.x = element_text(face = "italic"))  
Fig1_adiposity

statistical analysis

options(contrasts=c("contr.sum", "contr.poly"))
model_adiposity_gain <- lmerTest::lmer(adiposity_gain ~ Category*Sex + T0_fatperc*T0_weight +(1|Batch/Sex), data = metadata_gain)

Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -2.9e-03

lmerTest::step(model_adiposity_gain, keep = "Category")

Backward reduced random-effect table:

                Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)
<none>                       10 -406.30 832.61                     
(1 | Sex:Batch)          1    9 -406.30 830.61 0.0000  1     1.0000
(1 | Batch)              2    8 -407.33 830.66 2.0528  1     0.1519

Backward reduced fixed-effect table:
                     Eliminated Df Sum of Sq    RSS    AIC F value  Pr(>F)  
Category:Sex                  1  1     0.109 1729.2 381.59  0.0092 0.92387  
Sex                           2  1     0.141 1729.3 379.60  0.0119 0.91334  
T0_fatperc:T0_weight          3  1    15.273 1744.6 378.94  1.2983 0.25638  
T0_fatperc                    4  1     4.478 1749.0 377.33  0.3799 0.53861  
T0_weight                     5  1    37.965 1787.0 378.59  3.2342 0.07414 .
Category                      0  1     5.387 1792.4 377.05  0.4522 0.50234  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
adiposity_gain ~ Category

model_adiposity_gain.2 <- lm(adiposity_gain ~ Category+ T0_fatperc, data = metadata_gain)
stats::shapiro.test(resid(model_adiposity_gain.2)) # test normality of residuals

    Shapiro-Wilk normality test

data:  resid(model_adiposity_gain.2)
W = 0.99489, p-value = 0.8761

anova(lm(residuals(model_adiposity_gain.2)~fitted.values(model_adiposity_gain.2))) # test for heteroscedasticity 

Analysis of Variance Table

Response: residuals(model_adiposity_gain.2)
                                       Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_adiposity_gain.2)   1    0.0   0.000       0      1
Residuals                             150 1784.4  11.896               

plot(model_adiposity_gain.2)

options(contrasts=c("contr.sum", "contr.poly"))
car::Anova(model_adiposity_gain.2, test.statistic="F", type = "III")

Anova Table (Type III tests)

Response: adiposity_gain
             Sum Sq  Df F value    Pr(>F)    
(Intercept)  309.97   1 25.8828 1.076e-06 ***
Category       5.85   1  0.4887    0.4856    
T0_fatperc     2.58   1  0.2155    0.6432    
Residuals   1784.43 149                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

-> no treatment effect

Food intake and feeding efficiency

Promethion_Food <- Promethion.2 %>% 
  dplyr::filter(MouseID != 6438, MouseID != 6450) %>% # spilled food into cage, as mouse was not eating from hopper
  dplyr::filter(Date_common == as.POSIXct("2020-05-04 08:00:00")) %>% # filter endpoint data to obtain food eaten at the end of the experiment
  tidyr::drop_na(Promethion_weight) %>%
  dplyr::mutate(Food_corrected = FoodInA_M - Spilled_Food_cage, 
                FI_kcal = Food_corrected*3.406557, 
                FI_kcal_day = FI_kcal/70*24, 
                FI_kcal_day_BW = FI_kcal_day/Promethion_weight) %>%
  dplyr::group_by(Sex) %>% # exclude outliers, as spilled food might have measurement errors
  dplyr::mutate(lower_bound_FI_kcal_day_BW = median(FI_kcal_day_BW) - 3.5 *mad(FI_kcal_day_BW), 
                upper_bound_FI_kcal_day_BW = median(FI_kcal_day_BW) + 3.5 * mad(FI_kcal_day_BW)) %>%
  dplyr::ungroup() %>%
  dplyr::group_by(MouseID) %>%
  dplyr::filter(dplyr::between(FI_kcal_day_BW, lower_bound_FI_kcal_day_BW, upper_bound_FI_kcal_day_BW)) %>% 
  dplyr::ungroup() %>%
  dplyr::mutate(Feed_eficiency = ((T6_weight - T0_weight)/28)/FI_kcal_day)

Food intake

Fig1_foodintake_weight<- Promethion_Food %>%
  ggplot(aes(x= Promethion_weight ,y=FI_kcal_day, color = Category, group = Category)) +
  geom_smooth( method = "lm", se = F, alpha = 0.8, size = 0.5, show.legend = F)+ 
  geom_point(aes(shape = Sex), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=c(16, 4), name = "Sex", label = c("Female", "Male")) + 
  scale_color_manual(values=c(color_Category), label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  theme_classic(base_size = 10) +
  labs(y = "Food intake (kcal/d)", x = "Weight (g)") 
Fig1_foodintake_weight

Higher food intake
Statistical analysis:

set.seed(628)
Promethion_Food$FI_kcal_day_norm <- predict(bestNormalize(Promethion_Food$FI_kcal_day))
Promethion_Food$Promethion_weight_norm <- predict(bestNormalize(Promethion_Food$Promethion_weight))

options(contrasts=c("contr.sum", "contr.poly"))
model_food <- lmerTest::lmer(FI_kcal_day_norm ~ Category*Sex + Sex*Promethion_weight_norm + (Category|Batch/Sex/Promethion_Cabinet), data = Promethion_Food)

boundary (singular) fit: see help('isSingular')

lmerTest::step(model_food, keep = "Category")

boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -2.8e-03boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -1.9e-01boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                                                        Eliminated npar  logLik    AIC     LRT Df Pr(>Chisq)
<none>                                                               16 -219.15 470.29                      
Category in (Category | Sex:Batch)                               1   14 -219.15 466.29 0.00000  2     1.0000
(1 | Sex:Batch)                                                  2   13 -219.15 464.29 0.00000  1     1.0000
Category in (Category | Batch)                                   3   11 -219.15 460.29 0.00000  2     1.0000
Category in (Category | Promethion_Cabinet:(Sex:Batch))          4    9 -219.75 457.50 1.20694  2     0.5469
(1 | Promethion_Cabinet:(Sex:Batch))                             5    8 -219.75 455.50 0.00000  1     1.0000
(1 | Batch)                                                      6    7 -219.75 453.50 0.00072  1     0.9786

Backward reduced fixed-effect table:
                           Eliminated Df Sum of Sq    RSS     AIC F value    Pr(>F)    
Category:Sex                        1  1    0.0019 117.19 -53.244  0.0026 0.9593051    
Sex:Promethion_weight_norm          2  1    0.2236 117.41 -54.920  0.3148 0.5755352    
Category                            0  1    2.6423 120.05 -53.137  3.7358 0.0549586 .  
Sex                                 0  1    9.9013 127.31 -43.157 13.9988 0.0002517 ***
Promethion_weight_norm              0  1    3.9688 121.38 -51.269  5.6112 0.0189955 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
FI_kcal_day_norm ~ Category + Sex + Promethion_weight_norm

options(contrasts=c("contr.sum", "contr.poly"))
model_food.2 <- lm(FI_kcal_day_norm ~ Category+Sex+Promethion_weight_norm, data = Promethion_Food)
stats::shapiro.test(residuals(model_food.2))

    Shapiro-Wilk normality test

data:  residuals(model_food.2)
W = 0.98681, p-value = 0.1106

anova(lm(residuals(model_food.2)~fitted.values(model_food.2)))

Analysis of Variance Table

Response: residuals(model_food.2)
                             Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_food.2)   1   0.00 0.00000       0      1
Residuals                   168 117.41 0.69887               

plot(model_food.2)

options(contrasts=c("contr.sum", "contr.poly"))
car::Anova(model_food.2, test.statistic="F", type = "II")

Anova Table (Type II tests)

Response: FI_kcal_day_norm
                        Sum Sq  Df F value    Pr(>F)    
Category                 2.642   1  3.7358 0.0549586 .  
Sex                      9.901   1 13.9988 0.0002517 ***
Promethion_weight_norm   3.969   1  5.6112 0.0189955 *  
Residuals              117.411 166                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model_food_eff <- lm(FI_kcal_day ~ Category+Sex+Promethion_weight, data = Promethion_Food)
effects::effect("Category", model_food_eff, se =TRUE) %>% as.data.frame()

(14.25670-13.59437)

[1] 0.66233

(14.25670-13.59437)/13.59437*100

[1] 4.87209

model_food.residuals <- lm(FI_kcal_day_norm ~ Sex + Promethion_weight_norm, data = Promethion_Food)
Promethion_Food$FI_kcal_day_residuals <- residuals(model_food.residuals)/sigma(model_food.residuals)

Fig1_foodintake_resid <- Promethion_Food %>%
  ggplot(aes(x = Category, y = FI_kcal_day_residuals, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Adj. food intake") +
  theme(axis.text.x = element_text(face = "italic")) 
Fig1_foodintake_resid

Feed efficiency

Fig1_feed_eff<- Promethion_Food %>%
  ggplot(aes(x = Category, y = Feed_eficiency, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Feed efficicency (g/kcal)") +
  theme(axis.text.x = element_text(face = "italic")) 
Fig1_feed_eff

Lower feed efficiency Statistical analysis:

set.seed(8765)
Promethion_Food$Feed_eficiency_norm <- predict(bestNormalize(Promethion_Food$Feed_eficiency))

options(contrasts=c("contr.sum", "contr.poly"))
model_feed_eff <- lmer(Feed_eficiency_norm ~ Sex*Category + (Category |Batch/Sex/Promethion_Cabinet), data = Promethion_Food)

boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -1.8e-03

step(model_feed_eff, keep = "Category")

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -1.7e-03boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                                                        Eliminated npar  logLik    AIC     LRT Df Pr(>Chisq)    
<none>                                                               14 -227.99 483.98                          
Category in (Category | Sex:Batch)                               1   12 -227.99 479.98  0.0000  2     1.0000    
(1 | Sex:Batch)                                                  2   11 -227.99 477.98  0.0000  1     1.0000    
Category in (Category | Promethion_Cabinet:(Sex:Batch))          3    9 -228.01 474.02  0.0434  2     0.9786    
(1 | Promethion_Cabinet:(Sex:Batch))                             4    8 -228.01 472.02  0.0000  1     1.0000    
Category in (Category | Batch)                                   5    6 -228.40 468.79  0.7687  2     0.6809    
(1 | Batch)                                                      0    5 -243.68 497.35 30.5565  1  3.243e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

             Eliminated Sum Sq Mean Sq NumDF   DenDF F value  Pr(>F)  
Sex:Category          1 0.9106  0.9106     1 156.499  1.2589 0.26357  
Sex                   2 0.3354  0.3354     1  10.065  0.4628 0.51166  
Category              0 3.6370  3.6370     1 157.522  5.0187 0.02647 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Feed_eficiency_norm ~ Category + (1 | Batch)

model_feed_eff.2 <- lmer(Feed_eficiency_norm ~ Category  + (1 | Batch), data = Promethion_Food)
stats::shapiro.test(residuals(model_feed_eff.2))

    Shapiro-Wilk normality test

data:  residuals(model_feed_eff.2)
W = 0.9927, p-value = 0.5503

anova(lm(residuals(model_feed_eff.2)~fitted.values(model_feed_eff.2)))

Analysis of Variance Table

Response: residuals(model_feed_eff.2)
                                 Df  Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_feed_eff.2)   1   1.107  1.1073  1.6327 0.2031
Residuals                       168 113.937  0.6782               

plot(model_feed_eff.2)

options(contrasts=c("contr.sum", "contr.poly"))
car::Anova(model_feed_eff.2, test.statistic="F", type = "II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Feed_eficiency_norm
              F Df Df.res  Pr(>F)  
Category 5.0157  1 157.49 0.02652 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model_feed_eff_eff <- lmer(Feed_eficiency ~ Category  + (1 | Batch), data = Promethion_Food)
effects::effect("Category", model_feed_eff_eff)

 Category effect
Category
    Killed       Live 
0.01919142 0.01719512 

as.data.frame(effect("Category", model_feed_eff_eff, se = TRUE))

Fecal energy loss

metadata_bc <- metadata  %>%
  dplyr::mutate(bombcal_Calorific_Value_kcal = bombcal_Calorific_Value/4184, # convert bombcal_Calorific_Value from J/g to kcal/g
                bombcal_Calorific_Value_kcal_total = bombcal_Calorific_Value_kcal*bombcal_Dry_sample_mass,  # total energy content of 3 days in metabolic cages
                bombcal_Calorific_Value_kcal_total_day = bombcal_Calorific_Value_kcal_total/70*24, # calculate daily energy excretion
                bombcal_weight = bombcal_Calorific_Value_kcal_total_day / Promethion_weight) %>% # normalization by murine body weight for outlier detection
  tidyr::drop_na(bombcal_weight) %>%
  dplyr::group_by() %>%
  dplyr::mutate(lower_bound_bombcal_weight= median(bombcal_weight, rm.na = TRUE) - 3 *mad(bombcal_weight),
                upper_bound_bombcal_weight = median(bombcal_weight, rm.na = TRUE) + 3 * mad(bombcal_weight)) %>%
  dplyr::group_by(MouseID) %>%
  dplyr::filter(dplyr::between(bombcal_weight, lower_bound_bombcal_weight, upper_bound_bombcal_weight)) %>% # exclude outliers 
  dplyr::ungroup() 

Fecal mass

metadata_bc%>%
  ggplot(aes(x = Category, y = bombcal_Dry_sample_mass, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  labs(y = "Fecal mass (g)") +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  theme(axis.text.x = element_text(face = "italic")) 

set.seed(628)

metadata_bc$Promethion_weight_norm <- predict(bestNormalize(metadata_bc$Promethion_weight))
metadata_bc$bombcal_Dry_sample_mass_norm <- predict(bestNormalize(metadata_bc$bombcal_Dry_sample_mass))

options(contrasts=c("contr.sum", "contr.poly"))
model_fecal_mass <- lmerTest::lmer(bombcal_Dry_sample_mass_norm ~ Category*Sex + Promethion_weight_norm +(Category|Batch/Sex/Promethion_Cabinet), data = metadata_bc)

boundary (singular) fit: see help('isSingular')

lmerTest::step(model_fecal_mass, keep = "Category")

boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -4.6e-04boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -3.8e-01boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                                                        Eliminated npar  logLik    AIC     LRT Df Pr(>Chisq)    
<none>                                                               15 -110.05 250.10                          
Category in (Category | Sex:Batch)                               1   13 -110.05 246.10  0.0000  2   1.000000    
Category in (Category | Batch)                                   2   11 -110.05 242.10  0.0002  2   0.999893    
(1 | Sex:Batch)                                                  3   10 -110.05 240.10  0.0000  1   1.000000    
(1 | Batch)                                                      4    9 -110.05 238.10  0.0000  1   0.999987    
Category in (Category | Promethion_Cabinet:(Sex:Batch))          5    7 -110.28 234.56  0.4645  2   0.792758    
(1 | Promethion_Cabinet:(Sex:Batch))                             0    6 -116.09 244.19 11.6298  1   0.000649 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                       Eliminated Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
Category:Sex                    1 1.5791  1.5791     1 73.881  2.8956 0.0930256 .  
Sex                             2 0.7409  0.7409     1 19.157  1.3193 0.2648533    
Category                        0 0.2832  0.2832     1 75.248  0.5050 0.4795190    
Promethion_weight_norm          0 8.0941  8.0941     1 58.575 14.4345 0.0003475 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
bombcal_Dry_sample_mass_norm ~ Category + Promethion_weight_norm + (1 | Promethion_Cabinet:(Sex:Batch))

options(contrasts=c("contr.sum", "contr.poly"))
model_fecal_mass.2 <- lmer(bombcal_Dry_sample_mass_norm ~ Category + Promethion_weight_norm + (1 | Promethion_Cabinet:(Sex:Batch)), data = metadata_bc)
stats::shapiro.test(residuals(model_fecal_mass.2))

    Shapiro-Wilk normality test

data:  residuals(model_fecal_mass.2)
W = 0.969, p-value = 0.03315

anova(lm(residuals(model_fecal_mass.2)~fitted.values(model_fecal_mass.2)))

Analysis of Variance Table

Response: residuals(model_fecal_mass.2)
                                  Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_fecal_mass.2)  1  0.576 0.57595  1.1625  0.284
Residuals                         86 42.607 0.49543               

plot(model_fecal_mass.2)

car::Anova(model_fecal_mass.2, test.statistic="F", type = "II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: bombcal_Dry_sample_mass_norm
                             F Df Df.res    Pr(>F)    
Category                0.5036  1 75.173 0.4801212    
Promethion_weight_norm 13.1062  1 58.418 0.0006165 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model_fecal_mass_resid <- lmer(bombcal_Dry_sample_mass_norm ~ Sex + Promethion_weight_norm+ (1 | Promethion_Cabinet:(Sex:Batch)), data = metadata_bc)

metadata_bc$bombcal_Dry_sample_mass_resid <- residuals(model_fecal_mass_resid)/sigma(model_fecal_mass_resid)

FigS2_fecal_mass <-  metadata_bc%>%
  ggplot(aes(x = Category, y = bombcal_Dry_sample_mass_resid, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Adj. fecal mass") +
  theme(axis.text.x = element_text(face = "italic")) 
FigS2_fecal_mass

-> no difference between treatment groups

Bomb calorimetry

metadata_bc %>%
  ggplot(aes(x= Promethion_weight ,y=bombcal_Calorific_Value_kcal_total_day, color = Category, group = Category)) +
  geom_smooth( method = "lm", se = F, alpha = 0.8, size = 0.5, show.legend = F)+ 
  geom_point(aes(shape = Sex), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=c(16, 4), name = "Sex", label = c("Female", "Male")) + 
  scale_color_manual(values=c(color_Category), name = "Treatment", label = c("Killed-CM", "Live-CM")) +
  theme_classic(base_size = 10) +
  labs(y = "Fecal Energy (kcal/g/d)", x = "Weight (g)")

set.seed(628)

metadata_bc$bombcal_Calorific_Value_kcal_total_day_norm <- predict(bestNormalize(metadata_bc$bombcal_Calorific_Value_kcal_total_day))

options(contrasts=c("contr.sum", "contr.poly"))
model_Bomb_cal <- lmerTest::lmer(bombcal_Calorific_Value_kcal_total_day_norm ~ Category*Sex + Promethion_weight_norm +(Category|Batch/Sex/Promethion_Cabinet), data = metadata_bc)

boundary (singular) fit: see help('isSingular')

lmerTest::step(model_Bomb_cal, keep = "Category")

boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -9.5e-02boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -1.7e-01boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                                                        Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)   
<none>                                                               15 -112.34 254.67                        
Category in (Category | Sex:Batch)                               1   13 -112.34 250.67 0.0000  2    1.00000   
Category in (Category | Batch)                                   2   11 -112.35 246.70 0.0243  2    0.98793   
(1 | Sex:Batch)                                                  3   10 -112.35 244.70 0.0000  1    1.00000   
(1 | Batch)                                                      4    9 -112.35 242.70 0.0000  1    0.99998   
Category in (Category | Promethion_Cabinet:(Sex:Batch))          5    7 -112.42 238.83 0.1379  2    0.93336   
(1 | Promethion_Cabinet:(Sex:Batch))                             0    6 -116.55 245.10 8.2613  1    0.00405 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                       Eliminated  Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
Category:Sex                    1  1.1012  1.1012     1 74.155  1.8706 0.1755404    
Sex                             2  0.3468  0.3468     1 21.090  0.5803 0.4546367    
Category                        0  0.0772  0.0772     1 75.677  0.1295 0.7199197    
Promethion_weight_norm          0 10.4462 10.4462     1 49.255 17.5286 0.0001165 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
bombcal_Calorific_Value_kcal_total_day_norm ~ Category + Promethion_weight_norm + (1 | Promethion_Cabinet:(Sex:Batch))

options(contrasts=c("contr.sum", "contr.poly"))
model_Bomb_cal.2 <- lmer(bombcal_Calorific_Value_kcal_total_day_norm ~ Category + Promethion_weight_norm+ (1 | Promethion_Cabinet:(Sex:Batch)), data = metadata_bc)
stats::shapiro.test(residuals(model_Bomb_cal.2))

    Shapiro-Wilk normality test

data:  residuals(model_Bomb_cal.2)
W = 0.98781, p-value = 0.5867

anova(lm(residuals(model_Bomb_cal.2)~fitted.values(model_Bomb_cal.2)))

Analysis of Variance Table

Response: residuals(model_Bomb_cal.2)
                                Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_Bomb_cal.2)  1  0.575 0.57495  1.0799 0.3016
Residuals                       86 45.788 0.53242               

plot(model_Bomb_cal.2)

options(contrasts=c("contr.sum", "contr.poly"))
car::Anova(model_Bomb_cal.2, test.statistic="F", type = "II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: bombcal_Calorific_Value_kcal_total_day_norm
                             F Df Df.res    Pr(>F)    
Category                0.1291  1 75.434 0.7203622    
Promethion_weight_norm 15.7971  1 48.654 0.0002331 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model_Bomb_cal_resid <- lmer(bombcal_Calorific_Value_kcal_total_day_norm ~ Sex + Promethion_weight_norm+ (1 | Promethion_Cabinet:(Sex:Batch)), data = metadata_bc)
metadata_bc$bombcal_Calorific_Value_kcal_total_day_resid <- residuals(model_Bomb_cal_resid)/sigma(model_Bomb_cal_resid)

Fig1_fecal_energy <- metadata_bc%>%
  ggplot(aes(x = Category, y = bombcal_Calorific_Value_kcal_total_day_resid, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Adj. fecal energy conetnt") +
  theme(axis.text.x = element_text(face = "italic")) 
Fig1_fecal_energy

-> no difference between treatment groups ### Figure 1 - Effects of live or heat-killed C. minuta amendment to fecal transplants to GF mice on C. minuta, murine adiposity, feed efficiency and fecal energy loss four weeks post inoculation.

Legend_treatment_sex <- cowplot::get_legend(metadata_Cmin %>% 
  dplyr::mutate(Sex = ifelse(Sex == "F", "Female", "Male"), 
                Category = ifelse(Category == "Killed", "Killed-CM", "Live-CM"), 
                Sex_Category = paste(Sex, Category, sep = " ")) %>%
  ggplot(aes(x = Category, y = qPCR_Cmin_per_gCC, shape = Sex_Category, color = Sex_Category)) + 
  geom_point(aes(shape = Sex_Category, color= Sex_Category), alpha =0.7, size =0.5, show.legend = T) +
  scale_shape_manual(values=c(16, 16,4, 4)) + 
  scale_color_manual(values=c(color_Category, color_Category)) +
  theme_classic(base_size = 10) +
  guides(shape = guide_legend(override.aes = list(size = 3, alpha = 1))) +
  theme(legend.title=element_blank()))

Fig1_align <- cowplot::align_plots(Fig1_qPCR_Cmin + theme(legend.position="none"),
                                   Fig1_adiposity + theme(legend.position="none"),  
                                   Fig1_foodintake_weight + theme(legend.position="none"),
                                   Fig1_foodintake_resid + theme(legend.position="none"),
                                   Fig1_feed_eff + theme(legend.position="none"), 
                                   Fig1_fecal_energy + theme(legend.position="none"),
                                   align = 'hv', axis = 'tblr')

Warning: Removed 32 rows containing non-finite values (stat_summary).Warning: Removed 32 rows containing missing values (position_quasirandom).`geom_smooth()` using formula 'y ~ x'

                      
Fig1_1 <- cowplot::plot_grid(Fig1_align[[1]], 
                               Fig1_align[[2]],
                               Fig1_align[[5]],
                               Fig1_align[[6]],
                               ncol=2, nrow=2, labels = c("B", "C", "F", "G"),label_size = 12, rel_widths = c(1,1), rel_heights = c(1,1))

Fig1_2 <- cowplot::plot_grid(Fig1_align[[4]],
                               Legend_treatment_sex,
                               ncol=1, nrow=2, labels = c("E", NA),label_size = 12, rel_heights = c(1,1))

Fig1_3 <- cowplot::plot_grid(Fig1_align[[3]],
                               Fig1_2,
                               ncol=2, nrow=1, labels = c("D", NA),label_size = 12, rel_widths = c(1,1))

Warning: Removed 1 rows containing missing values (geom_text).

Figure1 <- cowplot::plot_grid(Fig1_1, 
                             Fig1_3,
                             ncol=2, nrow = 1, rel_widths = c(1,1))

Warning: Removed 1 rows containing missing values (geom_text).

Figure1

ggsave("./Figure1.pdf", plot = Figure1, device = cairo_pdf,  
  width = 17,
  height = 10.5,
  units = "cm")

Figure S2 - Murine body weight gain and fecal mass.

FigS2_align <- cowplot::align_plots(FigS2_weight + theme(legend.position="none"),
                                   FigS2_fecal_mass + theme(legend.position="none"),
                                   align = 'hv', axis = 'tblr')
                      
FigureS2 <- cowplot::plot_grid(FigS2_align[[1]], 
                               FigS2_align[[2]],
                               Legend_treatment_sex,
                               ncol=3, nrow=1, labels = c("A", "B", NA),label_size = 12, rel_widths = c(1.5,1, 0.75))

FigureS2

ggsave("./FigureS2.pdf", plot = Fig_supp.fe, device = cairo_pdf,  
  width = 13,
  height = 6.5,
  units = "cm")

C. minuta amended mice displayed higher physical activity and metabolic energy expenditure in a sex-dependent manner

Activity

For the activity data the acclimatization period is removed, as activity is highly influenced by stress

Promethion_activity_ee <- Promethion.2 %>% # create a common dataset to calculate EE and activity
  base::subset(Date_common >= as.POSIXct('2020-05-02 08:00:00') & Date_common <= as.POSIXct('2020-05-04 08:00:00')) %>% # exclude acclimatization period and end of experiment 
  dplyr::mutate(PedSpeed_Mnz = ifelse(PedSpeed_Mnz > 0, PedSpeed_Mnz, NA)) %>% # exclude speed when animals where not running
  dplyr::group_by(MouseID) %>%
  dplyr::summarise(Distance_traveled = sum(AllMeters_R), 
                   Beam_breaks = sum(XBreak_R) + sum(YBreak_R) + sum(ZBreak_R), 
                   mean_running_speed = mean(PedSpeed_Mnz, na.rm=TRUE),
                   EE_average_per_hour = mean(kcal_hr_M)) %>%
  dplyr::ungroup() %>%
  dplyr::left_join(metadata, by ="MouseID") 

Promethion_activity <- Promethion_activity_ee %>% # exclude mice with suspicious activity measurements 
  dplyr::group_by(Batch) %>% 
  dplyr::mutate(lower_bound_Distance_traveled = median(Distance_traveled) - 3 * mad(Distance_traveled),
                upper_bound_Distance_traveled = median(Distance_traveled) + 3 * mad(Distance_traveled),
                lower_bound_Beam_breaks = median(Beam_breaks) - 3 * mad(Beam_breaks), 
                upper_bound_Beam_breaks = median(Beam_breaks) + 3 * mad(Beam_breaks)) %>%
  dplyr::ungroup() %>%
  dplyr::group_by(MouseID) %>% 
  dplyr::filter(dplyr::between(Distance_traveled, lower_bound_Distance_traveled, upper_bound_Distance_traveled) | 
                dplyr::between(Beam_breaks, lower_bound_Beam_breaks, upper_bound_Beam_breaks)) %>%
  dplyr::ungroup() 

Beam breaks

Promethion_activity %>%
  ggplot(aes(x = Category, y = Beam_breaks, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "# Beam breaks") +
  theme(axis.text.x = element_text(face = "italic")) 

options(contrasts=c("contr.sum", "contr.poly"))
model_Beam_breaks<-(lmer(Beam_breaks ~ Category*Sex + (Category|Batch/Sex/Promethion_Cabinet), Promethion_activity))

boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -1.2e-03

step(model_Beam_breaks, keep ="Category")

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -1.5e-03boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                                                        Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)    
<none>                                                               14 -2043.9 4115.8                         
Category in (Category | Sex:Batch)                               1   12 -2043.9 4111.8  0.002  2     0.9991    
(1 | Sex:Batch)                                                  2   11 -2043.9 4109.8 -0.002  1     1.0000    
Category in (Category | Batch)                                   3    9 -2044.0 4105.9  0.147  2     0.9292    
Category in (Category | Promethion_Cabinet:(Sex:Batch))          4    7 -2044.3 4102.6  0.680  2     0.7116    
(1 | Promethion_Cabinet:(Sex:Batch))                             5    6 -2044.9 4101.8  1.191  1     0.2751    
(1 | Batch)                                                      0    5 -2071.5 4153.1 53.259  1  2.924e-13 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

             Eliminated     Sum Sq    Mean Sq NumDF   DenDF F value   Pr(>F)   
Category:Sex          1   22646547   22646547     1 169.185  0.0591 0.808218   
Category              0 2647923943 2647923943     1 170.178  6.9489 0.009163 **
Sex                   0 1917488030 1917488030     1  10.077  5.0320 0.048544 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Beam_breaks ~ Category + Sex + (1 | Batch)

options(contrasts=c("contr.sum", "contr.poly"))
model_Beam_breaks.2<-(lmerTest::lmer(Beam_breaks ~ Category + Sex  + (1 | Batch), Promethion_activity))
stats::shapiro.test(resid(model_Beam_breaks.2))

    Shapiro-Wilk normality test

data:  resid(model_Beam_breaks.2)
W = 0.99105, p-value = 0.3134

anova(lm(residuals(model_Beam_breaks.2)~fitted.values(model_Beam_breaks.2)))

Analysis of Variance Table

Response: residuals(model_Beam_breaks.2)
                                    Df     Sum Sq   Mean Sq F value Pr(>F)
fitted.values(model_Beam_breaks.2)   1 2.1460e+08 214596001  0.5981 0.4403
Residuals                          181 6.4937e+10 358767710               

plot(model_Beam_breaks.2)

options(contrasts=c("contr.sum", "contr.poly"))
car::Anova(model_Beam_breaks.2, type="II", test.statistic="F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Beam_breaks
              F Df Df.res   Pr(>F)   
Category 6.9480  1  170.1 0.009167 **
Sex      5.0319  1   10.0 0.048740 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

effects::effect("Category", model_Beam_breaks.2, se =TRUE) %>% as.data.frame()

(119692.2-112072.3)/112072.3*100

[1] 6.799093

model_Beam_breaks.residuals <-(lmerTest::lmer(Beam_breaks ~ Sex + (1 | Batch), Promethion_activity))
Promethion_activity$Beam_breaks_residuals <- residuals(model_Beam_breaks.residuals)/sigma(model_Beam_breaks.residuals)

Fig2_beam_breaks <- Promethion_activity %>% 
  ggplot(aes(x = Category, y = Beam_breaks_residuals, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Adj. beam breaks") +
  theme(axis.text.x = element_text(face = "italic")) 
Fig2_beam_breaks

-> significant more beam breaks by treatment

Distance traveled

Promethion_activity %>%
  ggplot(aes(x = Category, y = Distance_traveled, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Distance traveled (m)") +
  theme(axis.title.x=element_blank()) +
  theme(axis.text.x = element_text(face = "italic")) +
  facet_grid(Sex~.) +
     theme(strip.background = element_blank(),
        strip.text = element_blank())  +
  theme(axis.text.x = element_text(face = "italic")) 

–> although not statistically significant, exact analysis of data results in assumption of different treatment effects in males and females –> separate statistical analysis

options(contrasts=c("contr.sum", "contr.poly"))
Promethion_activity_M <- Promethion_activity %>% dplyr::filter(Sex =="M")
Promethion_activity_F <- Promethion_activity %>% dplyr::filter(Sex =="F")

model_Distance_traveled.m<-(lmerTest::lmer(Distance_traveled_norm ~ Category + (1 | Batch), Promethion_activity_M))
stats::shapiro.test(resid(model_Distance_traveled.m))

    Shapiro-Wilk normality test

data:  resid(model_Distance_traveled.m)
W = 0.97503, p-value = 0.08364

anova(lm(residuals(model_Distance_traveled.m)~fitted.values(model_Distance_traveled.m)))

Analysis of Variance Table

Response: residuals(model_Distance_traveled.m)
                                         Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_Distance_traveled.m)  1  0.214 0.21431  0.5092 0.4774
Residuals                                87 36.616 0.42087               

model_Distance_traveled.f<-(lmerTest::lmer(Distance_traveled_norm ~ Category + (1 | Batch), Promethion_activity_F))
stats::shapiro.test(resid(model_Distance_traveled.f))

    Shapiro-Wilk normality test

data:  resid(model_Distance_traveled.f)
W = 0.95915, p-value = 0.00503

anova(lm(residuals(model_Distance_traveled.f)~fitted.values(model_Distance_traveled.f)))

Analysis of Variance Table

Response: residuals(model_Distance_traveled.f)
                                         Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_Distance_traveled.f)  1  0.337 0.33669  0.5234 0.4712
Residuals                                92 59.180 0.64326               

car::Anova(model_Distance_traveled.m, test.statistic ="F", type="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Distance_traveled_norm
              F Df Df.res  Pr(>F)  
Category 4.6931  1 82.118 0.03318 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

car::Anova(model_Distance_traveled.f, test.statistic ="F", type="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Distance_traveled_norm
              F Df Df.res Pr(>F)
Category 0.0194  1     87 0.8897

model_Distance_traveled.meff<-(lmerTest::lmer(Distance_traveled ~ Category + (1 | Batch), Promethion_activity_M))
effects::effect("Category", model_Distance_traveled.meff, se =TRUE) %>% as.data.frame

model_Distance_traveled.feff<-(lmerTest::lmer(Distance_traveled ~ Category + (1 | Batch), Promethion_activity_F))
effects::effect("Category", model_Distance_traveled.feff, se =TRUE) %>% as.data.frame

model_Distance_traveled.m_residuals <-(lmerTest::lmer(Distance_traveled_norm ~ (1 | Batch), Promethion_activity_M))
model_Distance_traveled.f_residuals <-(lmerTest::lmer(Distance_traveled_norm ~ (1 | Batch), Promethion_activity_F))

Promethion_activity_M$Distance_traveled_residuals <- residuals(model_Distance_traveled.m_residuals)/sigma(model_Distance_traveled.m_residuals)
Promethion_activity_F$Distance_traveled_residuals <- residuals(model_Distance_traveled.f_residuals)/sigma(model_Distance_traveled.f_residuals)

Promethion_activity_MF<- rbind(Promethion_activity_M, Promethion_activity_F) 

Fig2_distance <- Promethion_activity_MF%>% 
  ggplot(aes(x = Category, y = Distance_traveled_residuals, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Adj. distance traveled", x= "Treatment") +
  theme(axis.title.x=element_blank()) +
  theme(axis.text.x = element_text(face = "italic")) +
  facet_grid(Sex~.) +
     theme(strip.background = element_blank(),
        strip.text = element_blank())  +
  theme(axis.text.x = element_text(face = "italic"))
Fig2_distance

Mean running speed

FigS3_speed <- Promethion_activity %>% 
  ggplot(aes(x = Category, y = mean_running_speed, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y ="Mean running speed (m/s)") +
  theme(axis.text.x = element_text(face = "italic")) 
FigS3_speed

set.seed(874)

Promethion_activity <- Promethion_activity %>%
  dplyr::mutate(mean_running_speed_norm = predict(bestNormalize(mean_running_speed)))


model_speed <- lmer(mean_running_speed_norm ~ Category*Sex + Promethion_Cabinet + (1|Batch), data = Promethion_activity)
step(model_speed)

Backward reduced random-effect table:

            Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)  
<none>                    7 -255.23 524.46                       
(1 | Batch)          0    6 -257.10 526.20 3.7438  1      0.053 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                   Eliminated Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
Category:Sex                1 0.0004  0.0004     1 168.17  0.0004 0.98380  
Promethion_Cabinet          2 0.1160  0.1160     1 168.75  0.1362 0.71259  
Category                    3 1.3672  1.3672     1 170.15  1.6129 0.20581  
Sex                         0 7.6179  7.6179     1   9.71  8.9641 0.01391 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
mean_running_speed_norm ~ Sex + (1 | Batch)

model_speed.2 <- lmer(mean_running_speed_norm ~ Category + Sex + (1|Batch), data = Promethion_activity)
shapiro.test(residuals(model_speed.2))

    Shapiro-Wilk normality test

data:  residuals(model_speed.2)
W = 0.98784, p-value = 0.1176

anova(lm(residuals(model_speed.2)~fitted.values(model_speed.2)))

Analysis of Variance Table

Response: residuals(model_speed.2)
                              Df  Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_speed.2)   1   0.915 0.91534  1.1278 0.2897
Residuals                    181 146.908 0.81165               

plot(model_speed.2)

car::Anova(model_speed.2, test.statistic ="F", type ="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: mean_running_speed_norm
              F Df  Df.res  Pr(>F)  
Category 1.6121  1 170.444 0.20593  
Sex      9.0239  1   9.995 0.01326 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model_speed.eff <- lmer(mean_running_speed ~ Category + Sex + (1|Batch), data = Promethion_activity)
effects::effect("Category", model_speed.eff, se =TRUE) %>% as.data.frame()

correlation of activity to C. minuta abundance

Promethion_activity_MF_Cmin<- Promethion_activity_MF %>% 
  dplyr::left_join(qPCR_data, by ="MouseID")

Promethion_activity_MF_Cmin %>%
  ggplot(aes(x= qPCR_Cmin_per_gCC ,y=Beam_breaks_residuals)) +
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5, color = "black")+ 
  geom_point(aes(shape = Sex, color = Category), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  theme_classic(base_size = 10) +
  labs(y= "Adj. beam breaks",
       x = "Norm. C. minuta") 

Promethion_activity_MF_Cmin %>%
  ggplot(aes(x= qPCR_Cmin_per_gCC ,y=Distance_traveled_residuals)) +
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5, color = "black")+ 
  geom_point(aes(shape = Sex, color = Category), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  theme_classic(base_size = 10) +
  labs(y= "Adj. distance traveled", 
       x = "Norm. C. minuta") +
   theme(strip.background = element_blank(),
        strip.text = element_blank()) +
  facet_wrap(.~Sex, scales = "free", ncol =1) 

set.seed(710)

Promethion_activity_MF_Cmin.2 <- Promethion_activity_MF_Cmin %>%
  tidyr::drop_na(qPCR_Cmin_per_gCC) %>%
  dplyr::mutate(qPCR_Cmin_per_gCC_norm = predict(bestNormalize(qPCR_Cmin_per_gCC)))

model_bb_cmin<-(lmer(Beam_breaks ~ qPCR_Cmin_per_gCC_norm*Sex + qPCR_Round + (1 |Batch/Sex/Promethion_Cabinet) + (1 |Batch:Promethion_Cabinet) + (1 |Promethion_Cabinet), Promethion_activity_MF_Cmin.2))

boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -3.3e-03

lmerTest::step(model_bb_cmin)

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -4.8e-04boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -4.2e-04Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -4.2e-04

Backward reduced random-effect table:

                                     Eliminated npar  logLik    AIC     LRT Df Pr(>Chisq)    
<none>                                            11 -2012.6 4047.2                          
(1 | Promethion_Cabinet:(Sex:Batch))          1   10 -2012.6 4045.2  0.0000  1     1.0000    
(1 | Promethion_Cabinet)                      2    9 -2012.6 4043.2  0.0000  1     1.0000    
(1 | Sex:Batch)                               3    8 -2012.6 4041.2  0.0000  1     1.0000    
(1 | Batch:Promethion_Cabinet)                4    7 -2013.2 4040.5  1.2422  1     0.2651    
(1 | Batch)                                   0    6 -2028.2 4068.4 29.9631  1  4.404e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                           Eliminated     Sum Sq    Mean Sq NumDF   DenDF F value  Pr(>F)  
qPCR_Cmin_per_gCC_norm:Sex          1    1144708    1144708     1 175.564  0.0029 0.95725  
qPCR_Round                          2  529054745  529054745     1  12.972  1.3388 0.26810  
qPCR_Cmin_per_gCC_norm              0 1871160245 1871160245     1 177.686  4.7537 0.03055 *
Sex                                 0 2365731335 2365731335     1   9.706  6.0102 0.03484 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Beam_breaks ~ qPCR_Cmin_per_gCC_norm + Sex + (1 | Batch)

model_distance_cmin<-(lmer(Distance_traveled ~ qPCR_Cmin_per_gCC_norm*Sex + qPCR_Round + (1 |Batch/Sex/Promethion_Cabinet) + (1 |Batch:Promethion_Cabinet) + (1 |Promethion_Cabinet), Promethion_activity_MF_Cmin.2))

boundary (singular) fit: see help('isSingular')

lmerTest::step(model_distance_cmin)

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -4.2e-03boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -4.2e-03boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -3.7e-01boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -3.3e-04Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -9.6e-05Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -9.6e-05

Backward reduced random-effect table:

                                     Eliminated npar  logLik    AIC     LRT Df Pr(>Chisq)    
<none>                                            11 -1072.6 2167.2                          
(1 | Promethion_Cabinet:(Sex:Batch))          1   10 -1072.6 2165.2  0.0000  1     1.0000    
(1 | Promethion_Cabinet)                      2    9 -1072.6 2163.2  0.0000  1     1.0000    
(1 | Sex:Batch)                               3    8 -1072.6 2161.2  0.0000  1     0.9999    
(1 | Batch:Promethion_Cabinet)                4    7 -1072.9 2159.8  0.6783  1     0.4102    
(1 | Batch)                                   0    6 -1087.7 2187.4 29.5386  1  5.481e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                           Eliminated Sum Sq Mean Sq NumDF   DenDF F value  Pr(>F)  
qPCR_Cmin_per_gCC_norm:Sex          1  13717   13717     1 175.348  1.5059 0.22141  
qPCR_Cmin_per_gCC_norm              2    222     222     1 173.631  0.0242 0.87662  
qPCR_Round                          3  17309   17309     1  11.898  1.8993 0.19352  
Sex                                 0  48172   48172     1  10.118  5.3086 0.04367 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Distance_traveled ~ Sex + (1 | Batch)

-> association between beam breaks and C. minuta for both sexes -> for distance traveled there might be only an association in males –> separate analysis #### Beam breaks ~ C. minuta

model_bb_cmin.2<-(lmer(Beam_breaks ~ qPCR_Cmin_per_gCC_norm + Sex + (1 | Batch), Promethion_activity_MF_Cmin.2))
shapiro.test(residuals(model_bb_cmin.2))

    Shapiro-Wilk normality test

data:  residuals(model_bb_cmin.2)
W = 0.98947, p-value = 0.2015

anova(lm(residuals(model_bb_cmin.2)~fitted.values(model_bb_cmin.2)))

Analysis of Variance Table

Response: residuals(model_bb_cmin.2)
                                Df     Sum Sq   Mean Sq F value Pr(>F)
fitted.values(model_bb_cmin.2)   1 2.2116e+08 221163295  0.5962  0.441
Residuals                      179 6.6399e+10 370944778               

car::Anova(model_bb_cmin.2, type ="II", test.statistic="F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Beam_breaks
                            F Df Df.res  Pr(>F)  
qPCR_Cmin_per_gCC_norm 4.6652  1 177.69 0.03212 *
Sex                    6.0101  1   9.91 0.03437 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

set.seed(765)
r2_model_bb_cmin_mar<- partR2(model_bb_cmin.2, partvars = c("qPCR_Cmin_per_gCC_norm"), 
                  R2_type = "marginal", nboot = 10)

  |                                                  | 0 % ~calculating  
  |+++++                                             | 10% ~01s          
  |++++++++++                                        | 20% ~01s          
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  |++++++++++++++++++++++++++++++++++++++++          | 80% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  

r2_model_bb_cmin_mar$R2 %>% dplyr::filter(term == "qPCR_Cmin_per_gCC_norm") %>% dplyr::select(estimate) %>% pull()

[1] 0.01843388

model_bb_cmin.resid<-(lmer(Beam_breaks ~ Sex + (1 | Batch), Promethion_activity_MF_Cmin.2))

Promethion_activity_MF_Cmin.2 <- Promethion_activity_MF_Cmin.2 %>% 
  dplyr::mutate(Beam_breaks_cmin_resid = residuals(model_bb_cmin.resid)/sigma(model_bb_cmin.resid))

Fig2_bb_cmin <- Promethion_activity_MF_Cmin.2 %>%
  ggplot(aes(x= qPCR_Cmin_per_gCC_norm ,y=Beam_breaks_cmin_resid)) +
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5, color = "black")+ 
  geom_point(aes(shape = Sex, color = Category), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  theme_classic(base_size = 10) +
  labs(y= "Adj. beam breaks",
       x = "Norm. C. minuta")
Fig2_bb_cmin

Distance traveled ~ C. minuta

Promethion_activity_M2 <- Promethion_activity_MF_Cmin.2 %>%
  dplyr::filter(Sex =="M") %>%
  dplyr::mutate( Batch_Promethion_Cabinet = paste( Batch, Promethion_Cabinet, sep ="_"))
Promethion_activity_F2 <- Promethion_activity_MF_Cmin.2 %>%
  dplyr::filter(Sex =="F")%>%
  dplyr::mutate( Batch_Promethion_Cabinet = paste( Batch, Promethion_Cabinet, sep ="_"))

model_distance_cmin_m<- lmer(Distance_traveled_norm ~ qPCR_Cmin_per_gCC_norm + (1 | Batch_Promethion_Cabinet), Promethion_activity_M2)
model_distance_cmin_f<- lmer(Distance_traveled_norm ~ qPCR_Cmin_per_gCC_norm + (1 | Batch_Promethion_Cabinet), Promethion_activity_F2)

shapiro.test(residuals(model_distance_cmin_m))

    Shapiro-Wilk normality test

data:  residuals(model_distance_cmin_m)
W = 0.98454, p-value = 0.3809

anova(lm(residuals(model_distance_cmin_m)~fitted.values(model_distance_cmin_m)))

Analysis of Variance Table

Response: residuals(model_distance_cmin_m)
                                     Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_distance_cmin_m)  1  0.713 0.71289   1.933  0.168
Residuals                            86 31.717 0.36880               

shapiro.test(residuals(model_distance_cmin_f))

    Shapiro-Wilk normality test

data:  residuals(model_distance_cmin_f)
W = 0.97406, p-value = 0.0606

anova(lm(residuals(model_distance_cmin_f)~fitted.values(model_distance_cmin_f)))

Analysis of Variance Table

Response: residuals(model_distance_cmin_f)
                                     Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_distance_cmin_f)  1  1.377 1.37658  2.3408 0.1295
Residuals                            91 53.515 0.58808               

plot(model_distance_cmin_m)

plot(model_distance_cmin_f)

car::Anova(model_distance_cmin_m, test.statistic ="F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Distance_traveled_norm
                            F Df Df.res Pr(>F)  
qPCR_Cmin_per_gCC_norm 4.3941  1 80.592 0.0392 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

car::Anova(model_distance_cmin_f, test.statistic ="F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Distance_traveled_norm
                            F Df Df.res Pr(>F)
qPCR_Cmin_per_gCC_norm 0.1132  1   90.3 0.7374

#r2 males
r2_model_distance_cmin_m_mar<- partR2(model_distance_cmin_m, partvars = c("qPCR_Cmin_per_gCC_norm"), 
                  R2_type = "marginal", nboot = 10)

  |                                                  | 0 % ~calculating  
  |+++++                                             | 10% ~01s          
  |++++++++++                                        | 20% ~01s          
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  |+++++++++++++++++++++++++++++++++++               | 70% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~00s          
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  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  

r2_model_distance_cmin_m_mar$R2 %>% dplyr::filter(term == "qPCR_Cmin_per_gCC_norm") %>% dplyr::select(estimate) %>% pull()

[1] 0.03734531

#r2 females
r2_model_distance_cmin_f_mar<- partR2(model_distance_cmin_f, partvars = c("qPCR_Cmin_per_gCC_norm"), 
                  R2_type = "marginal", nboot = 10)

  |                                                  | 0 % ~calculating  
  |+++++                                             | 10% ~02s          
  |++++++++++                                        | 20% ~01s          
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  |++++++++++++++++++++++++++++++++++++++++          | 80% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  

r2_model_distance_cmin_f_mar$R2 %>% dplyr::filter(term == "qPCR_Cmin_per_gCC_norm") %>% dplyr::select(estimate) %>% pull()

[1] 0.001360038

model_distance_cmin.resid_M<-(lmer(Distance_traveled_norm ~ + (1 | Batch:Promethion_Cabinet), Promethion_activity_M2))
model_distance_cmin.resid_F<-(lmer(Distance_traveled_norm ~ + (1 | Batch:Promethion_Cabinet), Promethion_activity_F2))

Promethion_activity_M2 <- Promethion_activity_M2 %>% 
  dplyr::mutate(Distance_traveled_cmin_resid = residuals(model_distance_cmin.resid_M)/sigma(model_distance_cmin.resid_M))
Promethion_activity_F2 <- Promethion_activity_F2 %>% 
  dplyr::mutate(Distance_traveled_cmin_resid = residuals(model_distance_cmin.resid_F)/sigma(model_distance_cmin.resid_F))

Promethion_activity_MF3 <- rbind(Promethion_activity_M2, Promethion_activity_F2)


Fig2_distance_cmin<- Promethion_activity_MF3 %>%
  ggplot(aes(x= qPCR_Cmin_per_gCC_norm ,y=Distance_traveled_cmin_resid)) +
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5, color = "black")+ 
  geom_point(aes(shape = Sex, color = Category), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  theme_classic(base_size = 10) +
  labs(y= "Adj. distance traveled", 
       x = "Norm. C. minuta") +
   theme(strip.background = element_blank(),
        strip.text = element_blank()) +
  facet_wrap(.~Sex, scales = "free", ncol =1) 
Fig2_distance_cmin

Correlate activity to feed efficiency

Promethion_activity_food <- Promethion_Food %>%
  dplyr::select(MouseID, Feed_eficiency) %>%
  dplyr::left_join(Promethion_activity, by = "MouseID") %>%
  tidyr::drop_na(Feed_eficiency, Beam_breaks)

Promethion_activity_food %>%
  ggplot(aes(x= Beam_breaks ,y=Feed_eficiency)) +
  geom_smooth(method = "lm", se = F, alpha = 0.8, size = 0.5)+ 
  geom_point(aes(shape = Sex, color = Category), alpha =0.7, size =0.5, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  theme_classic(base_size = 10) 

Promethion_activity_food %>%
  ggplot(aes(x= Distance_traveled ,y=Feed_eficiency)) +
  geom_smooth(method = "lm", se = F, alpha = 0.8, size = 0.5)+ 
  geom_point(aes(shape = Sex, color = Category), alpha =0.7, size =0.5, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  theme_classic(base_size = 10) +
  facet_grid(.~Sex)

options(contrasts=c("contr.sum", "contr.poly"))

model_fe_bb <- lmer(Feed_eficiency~ Beam_breaks*Sex + (1|Batch), Promethion_activity_food)

Warning: Some predictor variables are on very different scales: consider rescalingWarning: Some predictor variables are on very different scales: consider rescaling

step(model_fe_bb)

Warning: Some predictor variables are on very different scales: consider rescalingWarning: Some predictor variables are on very different scales: consider rescalingWarning: Some predictor variables are on very different scales: consider rescalingWarning: Some predictor variables are on very different scales: consider rescaling

Backward reduced random-effect table:

            Eliminated npar logLik     AIC    LRT Df Pr(>Chisq)    
<none>                    6 583.97 -1155.9                         
(1 | Batch)          0    5 577.26 -1144.5 13.411  1  0.0002501 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                Eliminated     Sum Sq    Mean Sq NumDF   DenDF F value Pr(>F)
Beam_breaks:Sex          1 7.7590e-05 7.7590e-05     1 160.461  2.5052 0.1154
Sex                      2 3.5060e-06 3.5060e-06     1   9.984  0.1137 0.7429
Beam_breaks              3 1.6137e-05 1.6137e-05     1 156.667  0.5237 0.4703

Model found:
Feed_eficiency ~ (1 | Batch)

model_fe_distance <- lmer(Feed_eficiency~ Distance_traveled*Sex + (1|Batch), Promethion_activity_food)
step(model_fe_distance)

Backward reduced random-effect table:

            Eliminated npar logLik     AIC    LRT Df Pr(>Chisq)    
<none>                    6 595.94 -1179.9                         
(1 | Batch)          0    5 587.54 -1165.1 16.792  1  4.171e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                      Eliminated     Sum Sq    Mean Sq NumDF  DenDF F value  Pr(>F)  
Distance_traveled:Sex          0 0.00014076 0.00014076     1 163.78  4.5929 0.03358 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Feed_eficiency ~ Distance_traveled * Sex + (1 | Batch)

set.seed(673)
Promethion_activity_food$Feed_eficiency_norm <- predict(bestNormalize(Promethion_activity_food$Feed_eficiency))

model_fe_bb.2 <- lmer(Feed_eficiency_norm~ Beam_breaks + (1|Batch), Promethion_activity_food)

Warning: Some predictor variables are on very different scales: consider rescalingWarning: Some predictor variables are on very different scales: consider rescaling

shapiro.test(residuals(model_fe_bb.2))

    Shapiro-Wilk normality test

data:  residuals(model_fe_bb.2)
W = 0.99334, p-value = 0.6357

anova(lm(residuals(model_fe_bb.2)~fitted.values(model_fe_bb.2)))

Analysis of Variance Table

Response: residuals(model_fe_bb.2)
                              Df  Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_fe_bb.2)   1   1.249 1.24913   1.807 0.1807
Residuals                    167 115.445 0.69129               

options(contrasts=c("contr.sum", "contr.poly"))
car::Anova(model_fe_bb.2, type ="II", test.statistic ="F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Feed_eficiency_norm
                 F Df Df.res Pr(>F)
Beam_breaks 0.3749  1 160.05 0.5412

r2_model_fe_bb_mar<- partR2(model_fe_bb.2, partvars = c("Beam_breaks"), 
                  R2_type = "marginal", nboot = 10)

  |                                                  | 0 % ~calculating  
  |+++++                                             | 10% ~01s          
  |++++++++++                                        | 20% ~01s          
  |+++++++++++++++                                   | 30% ~01s          
  |++++++++++++++++++++                              | 40% ~01s          
  |+++++++++++++++++++++++++                         | 50% ~01s          
  |++++++++++++++++++++++++++++++                    | 60% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  

r2_model_fe_bb_mar$R2 %>% dplyr::filter(term == "Beam_breaks") %>% dplyr::select(estimate) %>% pull()

[1] 0.002815192

model_fe_bb_resid <- lmer(Feed_eficiency_norm~ (1|Batch), Promethion_activity_food)

Promethion_activity_food <- Promethion_activity_food %>%
  dplyr::mutate(Feed_eficiency_bb_resid = residuals(model_fe_bb_resid)/sigma(model_fe_bb_resid))

Fig2_bb_fe <- Promethion_activity_food %>%
  ggplot(aes(x= (Beam_breaks/100000) ,y=Feed_eficiency_bb_resid))+
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5, color = "black")+ 
  geom_point(aes(color= Category, shape = Sex), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  labs(y="Adj. feed efficiency", x=expression(paste("Beam breaks (Nr) x ",e^5))) +
  theme_classic(base_size = 10) +
   theme(strip.background = element_blank(),
        strip.text = element_blank()) 
Fig2_bb_fe

Seperate models for each sex for Fe ~ distance

set.seed(673)
Promethion_activity_food_M <- Promethion_activity_food %>%
  dplyr::filter(Sex =="M")  %>%
  dplyr::mutate(Feed_eficiency_norm = predict(bestNormalize(Feed_eficiency)))

Promethion_activity_food_F <- Promethion_activity_food  %>%
  dplyr::filter(Sex =="F")  %>%
  dplyr::mutate(Feed_eficiency_norm = predict(bestNormalize(Feed_eficiency)))

model_fe_distance_m <- lmer(Feed_eficiency_norm~ Distance_traveled + (1|Batch), Promethion_activity_food_M)
model_fe_distance_f <- lmer(Feed_eficiency_norm~ Distance_traveled + (1|Batch), Promethion_activity_food_F)

shapiro.test(residuals(model_fe_distance_m))

    Shapiro-Wilk normality test

data:  residuals(model_fe_distance_m)
W = 0.99171, p-value = 0.8751

shapiro.test(residuals(model_fe_distance_f))

    Shapiro-Wilk normality test

data:  residuals(model_fe_distance_f)
W = 0.98873, p-value = 0.6757

anova(lm(residuals(model_fe_distance_m)~fitted.values(model_fe_distance_m)))

Analysis of Variance Table

Response: residuals(model_fe_distance_m)
                                   Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_fe_distance_m)  1  1.030 1.02994  1.2525 0.2663
Residuals                          82 67.427 0.82228               

anova(lm(residuals(model_fe_distance_f)~fitted.values(model_fe_distance_f)))

Analysis of Variance Table

Response: residuals(model_fe_distance_f)
                                   Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_fe_distance_f)  1  0.337 0.33706  0.5269 0.4699
Residuals                          83 53.093 0.63968               

car::Anova(model_fe_distance_m, test.statistic ="F", type ="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Feed_eficiency_norm
                       F Df Df.res  Pr(>F)  
Distance_traveled 2.8518  1 77.557 0.09529 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

car::Anova(model_fe_distance_f, test.statistic ="F", type ="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Feed_eficiency_norm
                       F Df Df.res Pr(>F)
Distance_traveled 1.3974  1 82.996 0.2405

#r2 males
r2_model_fe_distance_m_mar<- partR2(model_fe_distance_m, partvars = c("Distance_traveled"), 
                  R2_type = "marginal", nboot = 10)

  |                                                  | 0 % ~calculating  
  |+++++                                             | 10% ~01s          
  |++++++++++                                        | 20% ~01s          
  |+++++++++++++++                                   | 30% ~01s          
  |++++++++++++++++++++                              | 40% ~01s          
  |+++++++++++++++++++++++++                         | 50% ~01s          
  |++++++++++++++++++++++++++++++                    | 60% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  

r2_model_fe_distance_m_mar$R2 %>% dplyr::filter(term == "Distance_traveled") %>% dplyr::select(estimate) %>% pull()

[1] 0.03953071

#r2 females
r2_model_fe_distance_f_mar<- partR2(model_fe_distance_f, partvars = c("Distance_traveled"), 
                  R2_type = "marginal", nboot = 10)

  |                                                  | 0 % ~calculating  
  |+++++                                             | 10% ~02s          
  |++++++++++                                        | 20% ~01s          
  |+++++++++++++++                                   | 30% ~01s          
  |++++++++++++++++++++                              | 40% ~01s          
  |+++++++++++++++++++++++++                         | 50% ~01s          
  |++++++++++++++++++++++++++++++                    | 60% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  

r2_model_fe_distance_f_mar$R2 %>% dplyr::filter(term == "Distance_traveled") %>% dplyr::select(estimate) %>% pull()

[1] 0.01671485

model_fe_allm_m_resid <- lmer(Feed_eficiency_norm~  (1|Batch), Promethion_activity_food_M)
model_fe_allm_f_resid <- lmer(Feed_eficiency_norm~  (1|Batch), Promethion_activity_food_F)

Promethion_activity_food_M <- Promethion_activity_food_M %>%
  dplyr::mutate(Feed_eficiency_distance_resid = residuals(model_fe_allm_m_resid)/sigma(model_fe_allm_m_resid))
Promethion_activity_food_F <- Promethion_activity_food_F %>%
  dplyr::mutate(Feed_eficiency_distance_resid = residuals(model_fe_allm_f_resid)/sigma(model_fe_allm_f_resid))

Promethion_activity_food_MF <- rbind(Promethion_activity_food_M, Promethion_activity_food_F)


Fig2_fe_distance <- Promethion_activity_food_MF %>%
  ggplot(aes(x= (Distance_traveled/1000) ,y=Feed_eficiency_distance_resid))+
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5, color = "black")+ 
  geom_point(aes(color= Category, shape = Sex), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  labs(y="Adj. feed efficiency", x="Distance traveled (km)") +
  theme_classic(base_size = 10) +
  facet_wrap(.~Sex, scales = "free", ncol =1) + 
   theme(strip.background = element_blank(),
        strip.text = element_blank()) 
Fig2_fe_distance

Energy expenditure

Fig2_ee_weight<- Promethion_activity_ee %>%
  ggplot(aes(x= Promethion_weight ,y=EE_average_per_hour, color= Category, group = Category)) +
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5)+ 
  geom_point(aes(shape = Sex), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  theme_classic(base_size = 10) +
  labs(y = "Energy expenditure (kcal/h)", x = "Weight (g)") +
  facet_wrap(.~Sex, scales = "free", ncol =1) + 
  theme(strip.background = element_blank(),
   strip.text = element_blank()) 
Fig2_ee_weight

set.seed(719) 

Promethion_activity_ee.2 <- Promethion_activity_ee %>%
  drop_na(Promethion_weight)

Promethion_activity_ee.2$Promethion_weight_norm <- predict(bestNormalize(Promethion_activity_ee.2$Promethion_weight))
Promethion_activity_ee.2$EE_average_per_hour_norm<- predict(bestNormalize(Promethion_activity_ee.2$EE_average_per_hour))

options(contrasts=c("contr.sum", "contr.poly"))

model_ee<-(lmer(EE_average_per_hour_norm ~ Category*Sex + Sex*Promethion_weight_norm  + (Category |Batch/Sex/Promethion_Cabinet), Promethion_activity_ee.2))

boundary (singular) fit: see help('isSingular')

step(model_ee, keep ="Category")

boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -5.0e-03boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -1.3e-04boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                                                        Eliminated npar  logLik    AIC     LRT Df Pr(>Chisq)    
<none>                                                               16 -199.44 430.87                          
Category in (Category | Sex:Batch)                               1   14 -199.44 426.87  0.0000  2     1.0000    
(1 | Sex:Batch)                                                  2   13 -199.44 424.87  0.0000  1     1.0000    
Category in (Category | Promethion_Cabinet:(Sex:Batch))          3   11 -199.59 421.18  0.3045  2     0.8588    
Category in (Category | Batch)                                   4    9 -199.74 417.49  0.3097  2     0.8566    
(1 | Promethion_Cabinet:(Sex:Batch))                             5    8 -200.34 416.69  1.2039  1     0.2725    
(1 | Batch)                                                      0    7 -210.59 435.17 20.4843  1  6.012e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                           Eliminated Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
Sex:Promethion_weight_norm          1  0.007   0.007     1 165.965  0.0150    0.9027    
Category:Sex                        2  1.232   1.232     1 163.665  2.6868    0.1031    
Sex                                 3  0.378   0.378     1  16.250  0.8158    0.3796    
Category                            0  0.513   0.513     1 164.893  1.1067    0.2943    
Promethion_weight_norm              0 37.292  37.292     1  80.475 80.5037 9.491e-14 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
EE_average_per_hour_norm ~ Category + Promethion_weight_norm + (1 | Batch)

options(contrasts=c("contr.sum", "contr.poly"))
model_ee.2<-(lmer(EE_average_per_hour ~ Category*Sex + Promethion_weight + (1 | Batch), Promethion_activity_ee.2))
stats::shapiro.test(resid(model_ee.2))

    Shapiro-Wilk normality test

data:  resid(model_ee.2)
W = 0.98823, p-value = 0.1453

anova(lm(residuals(model_ee.2)~fitted.values(model_ee.2)))

Analysis of Variance Table

Response: residuals(model_ee.2)
                           Df   Sum Sq    Mean Sq F value Pr(>F)
fitted.values(model_ee.2)   1 0.000246 0.00024649  0.3533  0.553
Residuals                 176 0.122793 0.00069769               

Promethion_activity_ee.2_M <- Promethion_activity_ee.2 %>% dplyr::filter(Sex =="M")
Promethion_activity_ee.2_F <- Promethion_activity_ee.2 %>% dplyr::filter(Sex =="F")

model_ee.M<-(lmer(EE_average_per_hour ~ Category + Promethion_weight + (1 | Batch), Promethion_activity_ee.2_M))
model_ee.F<-(lmer(EE_average_per_hour ~ Category + Promethion_weight + (1 | Batch), Promethion_activity_ee.2_F))

shapiro.test(residuals(model_ee.M))

    Shapiro-Wilk normality test

data:  residuals(model_ee.M)
W = 0.9812, p-value = 0.2378

shapiro.test(residuals(model_ee.F))

    Shapiro-Wilk normality test

data:  residuals(model_ee.F)
W = 0.98739, p-value = 0.5334

aov(lm(residuals(model_ee.M)~fitted.values(model_ee.M)))

Call:
   aov(formula = lm(residuals(model_ee.M) ~ fitted.values(model_ee.M)))

Terms:
                fitted.values(model_ee.M)  Residuals
Sum of Squares                 0.00021583 0.06038527
Deg. of Freedom                         1         85

Residual standard error: 0.02665361
Estimated effects may be unbalanced

aov(lm(residuals(model_ee.F)~fitted.values(model_ee.F)))

Call:
   aov(formula = lm(residuals(model_ee.F) ~ fitted.values(model_ee.F)))

Terms:
                fitted.values(model_ee.F)  Residuals
Sum of Squares                 0.00014060 0.06272214
Deg. of Freedom                         1         89

Residual standard error: 0.026547
Estimated effects may be unbalanced

car::Anova(model_ee.M, test.statistic ="F", type ="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: EE_average_per_hour
                        F Df Df.res    Pr(>F)    
Category           0.1824  1 79.391    0.6705    
Promethion_weight 31.1667  1 72.787 3.847e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

car::Anova(model_ee.F, test.statistic ="F", type ="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: EE_average_per_hour
                        F Df Df.res    Pr(>F)    
Category           3.5387  1 83.064   0.06346 .  
Promethion_weight 31.5606  1 87.220 2.271e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

effects::effect("Category", mod = model_ee.M, se =TRUE) %>% as.data.frame()

effects::effect("Category", mod = model_ee.F, se =TRUE) %>% as.data.frame()

model_ee_residuals_M <-(lmer(EE_average_per_hour ~ Promethion_weight + (1 | Batch) , Promethion_activity_ee.2_M)) 
Promethion_activity_ee.2_M$EE_average_per_hour_residual <- residuals(model_ee_residuals_M)/sigma(model_ee_residuals_M)

model_ee_residuals_F <-(lmer(EE_average_per_hour ~  Promethion_weight + (1 | Batch) , Promethion_activity_ee.2_F)) 
Promethion_activity_ee.2_F$EE_average_per_hour_residual <- residuals(model_ee_residuals_F)/sigma(model_ee_residuals_F)

Promethion_activity_ee.2_MF <- rbind(Promethion_activity_ee.2_M, Promethion_activity_ee.2_F)

Fig2_ee_resid <- Promethion_activity_ee.2_MF %>%
  ggplot(aes(x = Category, y = EE_average_per_hour_residual, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Adj. energy expenditure") +
  facet_wrap(.~Sex, scales = "free_y", ncol =1) + 
  theme(strip.background = element_blank(),
   strip.text = element_blank()) +
  theme(axis.title.x=element_blank()) +
  theme(axis.text.x = element_text(face = "italic")) 
Fig2_ee_resid

Circadian

# Resting metabolic rate (RMR) for each cycle
Circadian_RMR_all <- Circadian.2 %>%
  dplyr::arrange(StartDate_common, desc(Cycle)) %>%
  dplyr::group_by(MouseID) %>%
  dplyr::summarise(All_RMR =mean(QR_EE_30)) %>%
  #dplyr::filter(Day == 3 & Cycle == 0) %>%
  dplyr::select(MouseID, All_RMR)%>%
  dplyr::ungroup()
Circadian_RMR_dark <- Circadian.2 %>% 
  dplyr::filter(Cycle == 0)  %>% # 0 = dark cycle = activity period of mice (nocturnal)
  dplyr::arrange(StartDate_common, desc(Cycle)) %>%
  dplyr::group_by(MouseID) %>%
  dplyr::summarise(Dark_RMR =mean(QR_EE_30))%>%
  #dplyr::filter(Day == 3) %>%
  dplyr::select(MouseID, Dark_RMR)%>%
  dplyr::ungroup()
Circadian_RMR_light <- Circadian.2 %>% 
  dplyr::filter(Cycle == 1)  %>% # 1 = light cycle = resting period of mice (nocturnal)
  dplyr::arrange(StartDate_common, desc(Cycle)) %>%
  dplyr::group_by(MouseID) %>%
  dplyr::summarise(Light_RMR =mean(QR_EE_30)) %>%
  #dplyr::filter(Day == 3) %>%
  dplyr::select(MouseID, Light_RMR)%>%
  dplyr::ungroup()

Circadian_RMR <- Circadian_RMR_all %>%
  dplyr::left_join(Circadian_RMR_dark, by ="MouseID") %>% 
  dplyr::left_join(Circadian_RMR_light, by ="MouseID") %>% 
  dplyr::left_join(metadata, by =c("MouseID")) %>%
  tidyr::drop_na(Promethion_weight)
  

set.seed(612)
options(contrasts=c("contr.sum", "contr.poly"))


Circadian_RMR$All_RMR_norm<- predict(bestNormalize::log_x(Circadian_RMR$All_RMR))
Circadian_RMR$Dark_RMR_norm<- predict(bestNormalize::log_x(Circadian_RMR$Dark_RMR))
Circadian_RMR$Light_RMR_norm<- predict(bestNormalize::log_x(Circadian_RMR$Light_RMR))
Circadian_RMR$Promethion_weight_norm<- predict(bestNormalize(Circadian_RMR$Promethion_weight))

Circadian_RMR_M <- Circadian_RMR %>% dplyr::filter(Sex == "M")
Circadian_RMR_F <- Circadian_RMR %>% dplyr::filter(Sex == "F")

model_rmr_all_m<-(lmer(All_RMR_norm ~ Category+ Promethion_weight_norm + (1 |Batch/Promethion_Cabinet), Circadian_RMR_M))

boundary (singular) fit: see help('isSingular')

model_rmr_dark_m<-(lmer(Dark_RMR_norm ~ Category+ Promethion_weight_norm + (1 |Batch/Promethion_Cabinet), Circadian_RMR_M))

boundary (singular) fit: see help('isSingular')

model_rmr_light_m<-(lmer(Light_RMR_norm ~ Category+ Promethion_weight_norm + (1 |Batch/Promethion_Cabinet), Circadian_RMR_M))

boundary (singular) fit: see help('isSingular')

model_rmr_all_f<-(lmer(All_RMR_norm ~ Category+ Promethion_weight_norm + (1 |Batch/Promethion_Cabinet), Circadian_RMR_F))

boundary (singular) fit: see help('isSingular')

model_rmr_dark_f<-(lmer(Dark_RMR_norm ~ Category+ Promethion_weight_norm + (1 |Batch/Promethion_Cabinet), Circadian_RMR_F))

boundary (singular) fit: see help('isSingular')

model_rmr_light_f<-(lmer(Light_RMR_norm ~ Category+ Promethion_weight_norm + (1 |Batch/Promethion_Cabinet), Circadian_RMR_F))

boundary (singular) fit: see help('isSingular')

step(model_rmr_all_m)

boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                               Eliminated npar  logLik    AIC     LRT Df Pr(>Chisq)
<none>                                       6 -114.98 241.96                      
(1 | Batch)                             1    5 -114.98 239.96 0.00000  1     1.0000
(1 | Promethion_Cabinet:Batch)          2    4 -115.14 238.28 0.32139  1     0.5708

Backward reduced fixed-effect table:
                       Eliminated Df Sum of Sq    RSS     AIC F value  Pr(>F)  
Category                        1  1    0.7969 66.433 -19.465  1.0199 0.31545  
Promethion_weight_norm          0  1    3.2012 69.634 -17.371  4.0959 0.04613 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
All_RMR_norm ~ Promethion_weight_norm

step(model_rmr_dark_m)

boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                               Eliminated npar  logLik    AIC     LRT Df Pr(>Chisq)
<none>                                       6 -117.97 247.94                      
(1 | Batch)                             1    5 -117.97 245.94 0.00000  1     1.0000
(1 | Promethion_Cabinet:Batch)          2    4 -118.20 244.40 0.45317  1     0.5008

Backward reduced fixed-effect table:
                       Eliminated Df Sum of Sq    RSS     AIC F value Pr(>F)
Promethion_weight_norm          1  1   0.69057 71.280 -13.339  0.8218 0.3673
Category                        2  1   1.08876 72.369 -14.020  1.2983 0.2577

Model found:
Dark_RMR_norm ~ 1

step(model_rmr_light_m)

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                               Eliminated npar  logLik   AIC LRT Df Pr(>Chisq)
<none>                                       6 -123.35 258.7                  
(1 | Promethion_Cabinet:Batch)          1    5 -123.35 256.7   0  1          1
(1 | Batch)                             2    4 -123.35 254.7   0  1          1

Backward reduced fixed-effect table:
                       Eliminated Df Sum of Sq    RSS     AIC F value  Pr(>F)  
Category                        1  1    0.0673 79.867 -3.4425  0.0709 0.79075  
Promethion_weight_norm          0  1    5.7019 85.569  0.5569  6.0683 0.01578 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Light_RMR_norm ~ Promethion_weight_norm

step(model_rmr_all_f)

Backward reduced random-effect table:

                               Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)    
<none>                                       6 -125.61 263.22                         
(1 | Promethion_Cabinet:Batch)          1    5 -125.61 261.22  0.000  1          1    
(1 | Batch)                             0    4 -133.39 274.79 15.562  1  7.982e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                       Eliminated Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)  
Category                        1 0.1508  0.1508     1 83.050  0.1911 0.66318  
Promethion_weight_norm          0 4.0853  4.0853     1 87.906  5.2239 0.02468 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
All_RMR_norm ~ Promethion_weight_norm + (1 | Batch)

step(model_rmr_dark_f)

Backward reduced random-effect table:

                               Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)    
<none>                                       6 -124.33 260.65                         
(1 | Promethion_Cabinet:Batch)          1    5 -124.33 258.65  0.000  1          1    
(1 | Batch)                             0    4 -133.52 275.03 18.382  1  1.807e-05 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                       Eliminated Sum Sq Mean Sq NumDF  DenDF F value Pr(>F)
Category                        1 1.1493  1.1493     1 83.039  1.5101 0.2226
Promethion_weight_norm          2 1.9050  1.9050     1 88.546  2.4892 0.1182

Model found:
Dark_RMR_norm ~ (1 | Batch)

step(model_rmr_light_f)

boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                               Eliminated npar  logLik    AIC     LRT Df Pr(>Chisq)
<none>                                       6 -123.38 258.77                      
(1 | Promethion_Cabinet:Batch)          1    5 -123.38 256.77 0.00000  1     1.0000
(1 | Batch)                             2    4 -123.47 254.94 0.17612  1     0.6747

Backward reduced fixed-effect table:
                       Eliminated Df Sum of Sq    RSS     AIC F value   Pr(>F)   
Category                        0  1    9.0518 82.852 -4.5356  10.793 0.001464 **
Promethion_weight_norm          0  1    8.6658 82.466 -4.9605  10.333 0.001827 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Light_RMR_norm ~ Category + Promethion_weight_norm

set.seed(149)

options(contrasts=c("contr.sum", "contr.poly"))
model_rmr_all_m2<-(lmer(All_RMR_norm ~ Category+ Promethion_weight_norm + (1 |Batch), Circadian_RMR_M))

boundary (singular) fit: see help('isSingular')

model_rmr_dark_m2<-(lmer(Dark_RMR_norm ~ Category+ Promethion_weight_norm + (1 |Batch), Circadian_RMR_M))

boundary (singular) fit: see help('isSingular')

model_rmr_light_m2<-(lmer(Light_RMR_norm ~ Category+ Promethion_weight_norm + (1 |Batch), Circadian_RMR_M))

boundary (singular) fit: see help('isSingular')

model_rmr_all_f2<-(lmer(All_RMR_norm ~ Category+ Promethion_weight_norm + (1 |Batch), Circadian_RMR_F))
model_rmr_dark_f2<-(lmer(Dark_RMR_norm ~ Category+ Promethion_weight_norm + (1 |Batch), Circadian_RMR_F))
model_rmr_light_f2<-(lmer(Light_RMR_norm ~ Category+ Promethion_weight_norm + (1 |Batch), Circadian_RMR_F))

shapiro.test(residuals(model_rmr_all_m2))

    Shapiro-Wilk normality test

data:  residuals(model_rmr_all_m2)
W = 0.97402, p-value = 0.07705

shapiro.test(residuals(model_rmr_dark_m2))

    Shapiro-Wilk normality test

data:  residuals(model_rmr_dark_m2)
W = 0.98664, p-value = 0.5151

shapiro.test(residuals(model_rmr_light_m2))

    Shapiro-Wilk normality test

data:  residuals(model_rmr_light_m2)
W = 0.97793, p-value = 0.1431

anova(lm(residuals(model_rmr_all_m2) ~ fitted.values(model_rmr_all_m2)))

Analysis of Variance Table

Response: residuals(model_rmr_all_m2)
                                Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_rmr_all_m2)  1  0.000 0.00000       0      1
Residuals                       85 65.636 0.77219               

anova(lm(residuals(model_rmr_dark_m2) ~ fitted.values(model_rmr_dark_m2)))

Analysis of Variance Table

Response: residuals(model_rmr_dark_m2)
                                 Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_rmr_dark_m2)  1  0.000 0.00000       0      1
Residuals                        85 70.589 0.83046               

anova(lm(residuals(model_rmr_light_m2) ~ fitted.values(model_rmr_light_m2)))

Analysis of Variance Table

Response: residuals(model_rmr_light_m2)
                                  Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_rmr_light_m2)  1    0.0 0.00000       0      1
Residuals                         85   79.8 0.93882               

shapiro.test(residuals(model_rmr_all_f2))

    Shapiro-Wilk normality test

data:  residuals(model_rmr_all_f2)
W = 0.98488, p-value = 0.376

shapiro.test(residuals(model_rmr_dark_f2))

    Shapiro-Wilk normality test

data:  residuals(model_rmr_dark_f2)
W = 0.98758, p-value = 0.5468

shapiro.test(residuals(model_rmr_light_f2))

    Shapiro-Wilk normality test

data:  residuals(model_rmr_light_f2)
W = 0.99356, p-value = 0.9414

anova(lm(residuals(model_rmr_all_f2) ~ fitted.values(model_rmr_all_f2)))

Analysis of Variance Table

Response: residuals(model_rmr_all_f2)
                                Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_rmr_all_f2)  1  0.326 0.32643  0.4416 0.5081
Residuals                       89 65.789 0.73921               

anova(lm(residuals(model_rmr_dark_f2) ~ fitted.values(model_rmr_dark_f2)))

Analysis of Variance Table

Response: residuals(model_rmr_dark_f2)
                                 Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_rmr_dark_f2)  1  0.318 0.31796  0.4467 0.5057
Residuals                        89 63.357 0.71187               

anova(lm(residuals(model_rmr_light_f2) ~ fitted.values(model_rmr_light_f2)))

Analysis of Variance Table

Response: residuals(model_rmr_light_f2)
                                  Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_rmr_light_f2)  1  0.047 0.04747   0.059 0.8087
Residuals                         89 71.638 0.80492               

plot(model_rmr_all_m2)
plot(model_rmr_dark_m2)
plot(model_rmr_light_m2)

plot(model_rmr_all_f2)
plot(model_rmr_dark_f2)
plot(model_rmr_light_f2)

options(contrasts=c("contr.sum", "contr.poly"))

results_model_rmr_all_m <- car::Anova(model_rmr_all_m2, test.statistic ="F", type ="II") %>% 
  as.data.frame() %>% 
  tibble::rownames_to_column(var = "model_term") %>%
  dplyr::mutate(Circ = "All", 
                Sex = "M") 
results_model_rmr_dark_m <- car::Anova(model_rmr_dark_m2, test.statistic ="F", type ="II") %>% 
  as.data.frame() %>% 
  tibble::rownames_to_column(var = "model_term") %>%
  dplyr::mutate(Circ = "Dark", 
                Sex = "M") 
results_model_rmr_light_m<-car::Anova(model_rmr_light_m2, test.statistic ="F", type ="II") %>% 
  as.data.frame() %>% 
  tibble::rownames_to_column(var = "model_term") %>%
  dplyr::mutate(Circ = "Light", 
                Sex = "M")

results_model_rmr_all_f <- car::Anova(model_rmr_all_f2, test.statistic ="F", type ="II") %>% 
  as.data.frame() %>% 
  tibble::rownames_to_column(var = "model_term") %>%
  dplyr::mutate(Circ = "All", 
                Sex = "F") 
results_model_rmr_dark_f <- car::Anova(model_rmr_dark_f2, test.statistic ="F", type ="II") %>% 
  as.data.frame() %>% 
  tibble::rownames_to_column(var = "model_term") %>%
  dplyr::mutate(Circ = "Dark", 
                Sex = "F") 
results_model_rmr_light_f <-car::Anova(model_rmr_light_f2, test.statistic ="F", type ="II") %>% 
  as.data.frame() %>% 
  tibble::rownames_to_column(var = "model_term") %>%
  dplyr::mutate(Circ = "Light", 
                Sex = "F")


rbind(results_model_rmr_all_m, results_model_rmr_dark_m, results_model_rmr_light_m, 
      results_model_rmr_all_f, results_model_rmr_dark_f, results_model_rmr_light_f) %>%
  dplyr::filter(model_term == "Category") 

model_rest_all.rmr_meff<-(lmer(All_RMR ~ Category+ Promethion_weight_norm + (1 |Batch), Circadian_RMR_M))

boundary (singular) fit: see help('isSingular')

model_rest_dark.rmr_meff<-(lmer(Dark_RMR ~ Category+ Promethion_weight_norm + (1 |Batch), Circadian_RMR_M))
model_rest_light.rmr_meff<-(lmer(Light_RMR ~ Category+ Promethion_weight_norm + (1 |Batch), Circadian_RMR_M))

boundary (singular) fit: see help('isSingular')

model_rest_all.rmr_feff<-(lmer(All_RMR ~ Category+ Promethion_weight_norm + (1 |Batch), Circadian_RMR_F))
model_rest_dark.rmr_feff<-(lmer(Dark_RMR ~ Category+ Promethion_weight_norm + (1 |Batch), Circadian_RMR_F))
model_rest_light.rmr_feff<-(lmer(Light_RMR ~ Category+ Promethion_weight_norm + (1 |Batch), Circadian_RMR_F))

eff_RMR_all_m<- effects::effect("Category", model_rest_all.rmr_meff, se = TRUE) %>% as.data.frame %>% dplyr::mutate(Circ ="All", Sex ="M")
eff_RMR_dark_m<- effects::effect("Category", model_rest_dark.rmr_meff, se = TRUE) %>% as.data.frame %>% dplyr::mutate(Circ ="dark", Sex ="M")
eff_RMR_light_m<- effects::effect("Category", model_rest_light.rmr_meff, se = TRUE) %>% as.data.frame %>% dplyr::mutate(Circ ="light", Sex ="M")

eff_RMR_all_f<- effects::effect("Category", model_rest_all.rmr_feff, se = TRUE) %>% as.data.frame %>% dplyr::mutate(Circ ="All", Sex ="F")
eff_RMR_dark_f<- effects::effect("Category", model_rest_dark.rmr_feff, se = TRUE) %>% as.data.frame %>% dplyr::mutate(Circ ="dark", Sex ="F")
eff_RMR_light_f<- effects::effect("Category", model_rest_light.rmr_feff, se = TRUE) %>% as.data.frame %>% dplyr::mutate(Circ ="light", Sex ="F")

rbind(eff_RMR_all_m, eff_RMR_dark_m, eff_RMR_light_m, eff_RMR_all_f, eff_RMR_dark_f, eff_RMR_light_f) %>% dplyr::select(Sex, Category, Circ, fit, se)

model_rest_all.rmr_m_resid<-(lmer(All_RMR_norm ~  Promethion_weight_norm + (1 |Batch), Circadian_RMR_M))

boundary (singular) fit: see help('isSingular')

model_rest_dark.rmr_m_resid<-(lmer(Dark_RMR_norm ~  Promethion_weight_norm + (1 |Batch), Circadian_RMR_M))
model_rest_light.rmr_m_resid<-(lmer(Light_RMR_norm ~  Promethion_weight_norm + (1 |Batch), Circadian_RMR_M))

boundary (singular) fit: see help('isSingular')

model_rest_all.rmr_f_resid<-(lmer(All_RMR_norm ~  Promethion_weight_norm + (1 |Batch), Circadian_RMR_F))
model_rest_dark.rmr_f_resid<-(lmer(Dark_RMR_norm ~ Promethion_weight_norm + (1 |Batch), Circadian_RMR_F))
model_rest_light.rmr_f_resid<-(lmer(Light_RMR_norm ~  Promethion_weight_norm + (1 |Batch), Circadian_RMR_F))

Circadian_RMR_M2 <- Circadian_RMR_M %>% 
  dplyr::mutate(all_rmr_resid = residuals(model_rest_all.rmr_m_resid)/sigma(model_rest_all.rmr_m_resid), 
                dark_rmr_resid = residuals(model_rest_dark.rmr_m_resid)/sigma(model_rest_dark.rmr_m_resid),
                light_rmr_resid = residuals(model_rest_light.rmr_m_resid)/sigma(model_rest_light.rmr_m_resid))
Circadian_RMR_F2 <- Circadian_RMR_F %>% 
  dplyr::mutate(all_rmr_resid = residuals(model_rest_all.rmr_f_resid)/sigma(model_rest_all.rmr_f_resid), 
                dark_rmr_resid = residuals(model_rest_dark.rmr_f_resid)/sigma(model_rest_dark.rmr_f_resid),
                light_rmr_resid = residuals(model_rest_light.rmr_f_resid)/sigma(model_rest_light.rmr_f_resid))

Circadian_rmr_resid <- rbind(Circadian_RMR_M2, Circadian_RMR_F2) %>%
  dplyr::select(MouseID, dark_rmr_resid, light_rmr_resid, all_rmr_resid) %>%
  dplyr::rename(Dark = dark_rmr_resid, Light = light_rmr_resid, All = all_rmr_resid) %>%
  tidyr::gather(Circ, mean_resting_metabolic_rate_residuals, Dark:All) %>%
  dplyr::left_join(metadata, by = c("MouseID"))

Fig2_rmr_dark_light <- Circadian_rmr_resid %>% 
  dplyr::filter(Circ != "All") %>% 
  ggplot(aes(x = Category, y = mean_resting_metabolic_rate_residuals, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  theme(axis.text.x = element_text(face = "italic")) +
    facet_grid(Sex~Circ) + 
  theme(#strip.background = element_blank(),
   strip.text.y = element_blank()) +
  labs(y="Adj. resting metabolic rate") 
Fig2_rmr_dark_light

FigS3_rmr_all <- Circadian_rmr_resid %>% 
  dplyr::filter(Circ == "All") %>%  
  ggplot(aes(x = Category, y = mean_resting_metabolic_rate_residuals, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  theme(axis.text.x = element_text(face = "italic")) +
    facet_grid(.~Sex) + 
  theme(strip.background = element_blank(),
   strip.text = element_blank()) +
  labs(y="Adj. resting metabolic rate") 
FigS3_rmr_all

Figure 2: Higher physical activity and metabolic energy expenditure in mice with live C. minuta

Fig2_legend <- cowplot::get_legend(metadata_Cmin %>% 
  dplyr::mutate(Sex = ifelse(Sex == "F", "Female", "Male"), 
                Category = ifelse(Category == "Killed", "Killed-CM", "Live-CM"), 
                Sex_Category = paste(Sex, Category, sep = " ")) %>%
  ggplot(aes(x = Category, y = qPCR_Cmin_per_gCC, shape = Sex_Category, color = Sex_Category)) + 
  geom_point(aes(shape = Sex_Category, color= Sex_Category), alpha =0.7, size =0.5, show.legend = T) +
  scale_shape_manual(values=c(16, 16,4, 4)) + 
  scale_color_manual(values=c(color_Category, color_Category)) +
  theme_classic(base_size = 10) +
  guides(shape = guide_legend(override.aes = list(size = 3, alpha = 1))) +
  theme(legend.title=element_blank()) + 
  theme(legend.position = "bottom"))

Fig2_align_1 <- cowplot::align_plots(Fig2_beam_breaks + theme(legend.position="none"),
                                 Fig2_bb_cmin + theme(legend.position="none"),
                                 Fig2_bb_fe + theme(legend.position="none"),
                                 Fig2_distance + theme(legend.position="none"),  
                                 Fig2_distance_cmin + theme(legend.position="none"), 
                                 Fig2_fe_distance + theme(legend.position="none"),
                                 align = 'hv', axis = 'tblr')

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Fig2_align_2 <- cowplot::align_plots(Fig2_ee_weight + theme(legend.position="none"),
                                 Fig2_ee_resid + theme(legend.position="none"),
                                 Fig2_rmr_dark_light + theme(legend.position="none"),
                                 align = 'hv', axis = 'tblr')

`geom_smooth()` using formula 'y ~ x'
Warning: Removed 6 rows containing non-finite values (stat_smooth).Warning: Removed 6 rows containing missing values (geom_point).

Fig2_1 <- cowplot::plot_grid(NA,
                               Fig2_align_1[[1]],  
                               Fig2_align_1[[2]], 
                               Fig2_align_1[[3]], 
                               NA, 
                               Fig2_align_1[[4]], 
                               Fig2_align_1[[5]], 
                               Fig2_align_1[[6]], 
                               ncol=4, nrow=2, labels = c(NA,"A", "C", "E", NA, "B", "D", "F"), label_size = 12, rel_widths = c(0.3, 1, 1.5, 1.5), rel_heights = c(1.2, 2))

Warning: Cannot convert object of class logical into a grob.Warning: Cannot convert object of class logical into a grob.

Fig2_2 <- cowplot::plot_grid(NA, 
                               Fig2_align_2[[1]], 
                               Fig2_align_2[[2]], 
                               Fig2_align_2[[3]], 
                               ncol=4, nrow=1, labels = c(NA, "G", "H", "I"),label_size = 12, rel_widths = c(0.3, 1.2, 1, 1.8))

Warning: Cannot convert object of class logical into a grob.

Figure2 <- cowplot::plot_grid(Fig2_1, 
                             Fig2_2, 
                             Fig2_legend, 
                             ncol=1, nrow=3, rel_heights = c(3.2, 2, 0.3))

Warning: Removed 1 rows containing missing values (geom_text).Warning: Removed 1 rows containing missing values (geom_text).Warning: Removed 1 rows containing missing values (geom_text).

Figure2

ggsave("./Figure2.pdf", plot = Figure2, device = cairo_pdf,  
  width = 17,
  height = 19,
  units = "cm")

Figure S3: Mouse voluntary activity and average resting metabolic rate

FigS3_align <- cowplot::align_plots(FigS3_speed + theme(legend.position="none"),
                                      FigS3_rmr_all + theme(legend.position="none"), 
                                      align = 'hv', axis = 'tblr')


FigS3_1 <- cowplot::plot_grid(FigS3_align[[1]], 
                                    FigS3_align[[2]],
                                    ncol=2, nrow=1, labels = c("A", "B"), label_size = 12, rel_widths = c(0.9, 1.5))
FigS3 <- cowplot::plot_grid(FigS3_1, 
                                    Fig2_legend,
                                    ncol=1, nrow=2, rel_heights = c(1, 0.1 ))

FigS3

ggsave("./FigS3.pdf", plot = FigS3, device = cairo_pdf,  
  width = 9,
  height = 7.5,
  units = "cm")

C. minuta increases gut microbial biomass and remodels diversity

Microbial biomass

Fig3_qPCR_bacteria <- metadata_Cmin %>% 
  dplyr::mutate(SQ_per_gCC_e10 = qPCR_16S_SQ_per_gCC/10^10) %>%
  ggplot(aes(x = Category, y = SQ_per_gCC_e10, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = expression(paste("Microbes (GE/g) x", e^10))) +
  theme(axis.text.x = element_text(face = "italic")) 
Fig3_qPCR_bacteria

set.seed(759)
metadata_Cmin <- metadata_Cmin %>% 
  dplyr::mutate(qPCR_16S_SQ_per_gCC = as.numeric(qPCR_16S_SQ_per_gCC), 
                qPCR_16S_SQ_per_gCC_norm = predict(bestNormalize(qPCR_16S_SQ_per_gCC)))

options(contrasts=c("contr.sum", "contr.poly"))
model_biomass<- lmerTest::lmer(qPCR_16S_SQ_per_gCC_norm ~ Category*Sex + T6_weight + Plate + (1|qPCR_Round/Batch/Sex), data = metadata_Cmin)

Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 2 negative eigenvaluesWarning: Model failed to converge with 2 negative eigenvalues: -1.4e-08 -1.1e-03

lmerTest::step(model_biomass, keep = "Category")

Warning: Model is nearly unidentifiable: large eigenvalue ratio
 - Rescale variables?Warning: Model is nearly unidentifiable: large eigenvalue ratio
 - Rescale variables?Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -8.1e-04Warning: Model is nearly unidentifiable: large eigenvalue ratio
 - Rescale variables?

Backward reduced random-effect table:

                             Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)  
<none>                                    11 -251.44 524.89                       
(1 | Sex:(Batch:qPCR_Round))          1   10 -251.44 522.89 0.0000  1    1.00000  
(1 | qPCR_Round)                      2    9 -251.44 520.89 0.0000  1    1.00000  
(1 | Batch:qPCR_Round)                0    8 -253.30 522.60 3.7152  1    0.05392 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

             Eliminated Sum Sq Mean Sq NumDF   DenDF F value  Pr(>F)  
Category:Sex          1 0.0785  0.0785     1 166.180  0.0945 0.75893  
T6_weight             2 0.3091  0.3091     1 171.758  0.3742 0.54151  
Sex                   3 1.1350  1.1350     1   9.694  1.3783 0.26842  
Category              0 5.3811  5.3811     1 168.188  6.5377 0.01145 *
Plate                 0 8.2627  4.1314     2  19.773  5.0193 0.01726 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
qPCR_16S_SQ_per_gCC_norm ~ Category + Plate + (1 | Batch:qPCR_Round)

model_biomass.2<- lmer(qPCR_16S_SQ_per_gCC_norm ~ Category + Plate + (1 | Batch:qPCR_Round), data = metadata_Cmin)

shapiro.test(residuals(model_biomass.2))

    Shapiro-Wilk normality test

data:  residuals(model_biomass.2)
W = 0.9801, p-value = 0.01094

anova(lm(residuals(model_biomass.2) ~ fitted(model_biomass.2))) # test for heteroscedasticity 

Analysis of Variance Table

Response: residuals(model_biomass.2)
                         Df  Sum Sq Mean Sq F value Pr(>F)
fitted(model_biomass.2)   1   0.764 0.76419  0.9759 0.3245
Residuals               179 140.169 0.78307               

plot(model_biomass.2)

car::Anova(model_biomass.2, type = "II", test.statistic ="F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: qPCR_16S_SQ_per_gCC_norm
              F Df  Df.res  Pr(>F)  
Category 6.5313  1 167.738 0.01149 *
Plate    4.8712  2  24.506 0.01655 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model_biomass.eff<- lmer(qPCR_16S_SQ_per_gCC~ Category + Plate + (1 | Batch:qPCR_Round), data = metadata_Cmin)

Warning: Model may not have converged with 1 eigenvalue close to zero: 1.8e-17

effects::effect("Category", model_biomass.eff, se =TRUE) %>% as.data.frame

Corrleation microbial biomass ~ C. minuta

Fig3_Cmin_biomass<- metadata_Cmin %>%
  ggplot(aes(x= qPCR_Cmin_per_gCC_norm ,y=qPCR_16S_SQ_per_gCC_norm)) +
  geom_smooth(method = "lm", se = F, alpha = 0.5, size = 0.5, color ="black")+ 
  geom_point(aes(shape = Sex, color =Category), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  theme_classic(base_size = 10) +
  stat_cor(method = "spearman",  cor.coef.name = c("rho"), size = 3) +
  labs(y = "Norm. Microbes", x = "Norm. C. minuta")
Fig3_Cmin_biomass

Modification of profiled metagenomic data

# Unrarefied OTUs
reads_unrare <- Bracken_otu_table_reads %>%
  dplyr::select(OTU_ID, contains("CC"), # select only cecal content samples
                -taxonomy, -contains(Metagenomics_exclude_id)) %>% 
  dplyr::rename(rowname = "OTU_ID") %>% 
  tidyr::gather(var, value, -rowname) %>% 
  tidyr::spread(rowname, value) %>%
  tibble::column_to_rownames(var = "var") 

names(reads_unrare) <- properColnames(reads_unrare)

# Rarefied OTUs
set.seed(2)
reads_rare = reads_unrare %>% 
 GUniFrac::Rarefy(460000)
reads_rare = reads_rare$otu.tab.rff %>% 
  as.data.frame()

Reads normalization by micriobial biomass (approximation to absolute abundance)

reads_rare_norm <- reads_rare %>%
  tibble::rownames_to_column(var = "rowname") %>% 
  tidyr::gather(var, value, -rowname) %>% 
  tidyr::spread(rowname, value) %>% 
  dplyr::rename(OTU_ID = var) 

reads_rare_norm[,2:183] <- as.data.table(lapply(reads_rare_norm[,2:183], as.numeric))

reads_rare_norm <- reads_rare_norm %>%
  dplyr::rename(rowname = OTU_ID) %>%
  dplyr::mutate_if(is.numeric,function(x) (x/sum(x))) %>%
  tidyr::gather(var, value, -rowname) %>% 
  tidyr::spread(rowname, value) %>% 
  dplyr::rename(sampleid = var) %>%
  dplyr::left_join(Metagenomics_metadata_CC, by ="sampleid") %>%
  dplyr::left_join(metadata, by ="MouseID") %>%
  dplyr::left_join(qPCR_data, by ="MouseID") %>%
  dplyr::mutate(qPCR_16S_SQ_per_gCC = as.numeric(qPCR_16S_SQ_per_gCC))  %>%
  dplyr::group_by(sampleid) %>%
  dplyr::mutate_each(funs(.*qPCR_16S_SQ_per_gCC), starts_with("s__"))

write.table(reads_rare_norm, file = "./data/Interim_output/reads_rare_norm.txt",row.names=FALSE,sep="\t", quote = FALSE)

reads_rare_norm<- fread("./data/Interim_output/reads_rare_norm.txt") 

Taxa barplots

Taxonomy <- Bracken_otu_table_reads %>%
  dplyr::select(OTU_ID, taxonomy) %>%
  dplyr::mutate(var = OTU_ID) 

Taxonomy$var <-properNames(Taxonomy$var)
Taxonomy$OTU_ID <- gsub(" ", "_", Taxonomy$OTU_ID)

reads_rare_norm_metadata <- reads_rare_norm %>%
  tibble::column_to_rownames(var = "sampleid") %>%
  dplyr::select(-contains("s__"))

reads_rare_norm_reads <- reads_rare_norm %>%
  dplyr::select(sampleid, contains("s__")) %>%
  tidyr::gather(var, value, -sampleid) %>% 
  tidyr::spread(sampleid, value) %>%
  dplyr::mutate_if(is.numeric, round) %>%
  tibble::column_to_rownames(var = "var")

# Separate taxonomy
tax_levels = c("Domain", "Phylum", "Class", "Order", "Family", "Genus", "Species")
# Also note that the unclassified reads do not count for the total seq count!

MF_bracken <- reads_rare_norm_reads %>%
  tibble::rownames_to_column(var = "var") %>%
  dplyr::mutate_at(c(2:183), as.numeric) %>%
  dplyr::left_join(Taxonomy, by ="var") %>% 
  dplyr::rename(name = var) %>% 
  dplyr::filter(name != "Unclassified") %>% 
  tidyr::separate(taxonomy, into = tax_levels, sep = ";") 

# Total counts for relative abundance
Total_counts = MF_bracken %>% 
  dplyr::select_if(is.numeric) %>% 
  dplyr::summarise_all(sum)

# summarize genus
Taxa_df = MF_bracken %>%
  dplyr::group_by(Genus) %>% 
  dplyr::summarise_if(is.numeric, sum) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename("Taxon" = "Genus")

# Bind tables and transform to relative abundance
Taxa_df_raw = Taxa_df %>% 
  dplyr::rowwise() %>% 
  dplyr::mutate(mean_abund = mean(c_across(where(is.numeric)))) 

# Filter taxa with mean abund < 0.5
Taxa_vec = Taxa_df_raw %>% 
  dplyr::filter(mean_abund >= 100000000) %>% 
  dplyr::pull(Taxon)

# Create df of abundant taxa
Abundant_df = Taxa_df_raw %>% 
  dplyr::filter(Taxon %in% Taxa_vec)

# Collapse rare taxa into 'other'
Rare_df = Taxa_df_raw %>% 
  ungroup() %>% 
  dplyr::filter(!(Taxon %in% Taxa_vec)) %>% 
  dplyr::summarise_if(is.numeric, sum) %>% 
  dplyr::mutate(Taxon = "Other") %>% 
  dplyr::select(Taxon, everything())

# Join and melt
Taxa_df.2 = bind_rows(Abundant_df, Rare_df) %>% 
  dplyr::select(-mean_abund) %>% 
  tidyr::pivot_longer(cols = -Taxon, names_to = "Sample", values_to = "Abundance")

metadata_taxa_barplot <- metadata %>%
  dplyr::select(MouseID, Category, Batch) %>%
  dplyr::group_by(Category, Batch) %>%
  dplyr::mutate(Mouse_nr_Category_Batch = row_number()) %>%
  dplyr::ungroup() %>%
  dplyr::select(MouseID, Mouse_nr_Category_Batch)

Taxa_barplot <- reads_rare_norm_metadata %>%
  tibble::rownames_to_column(var = "Sample") %>%
  dplyr::left_join(Taxa_df.2, by = "Sample") %>%
  dplyr::left_join(metadata_taxa_barplot, by = "MouseID") %>%
  dplyr::mutate(Abundance = as.numeric(Abundance))

taxabarplot_levels <- reads_rare_norm_metadata %>% 
  dplyr::arrange(Category, -qPCR_16S_SQ_per_gCC) %>%
  dplyr::pull(MouseID)

FigS4_Taxa_barplot<- Taxa_barplot %>%
  dplyr::mutate(Abundance = as.numeric(Abundance)) %>%
  tidyr::separate(Taxon, into =c(NA, "Taxon"), sep= "__") %>%
  dplyr::mutate(Taxon = replace_na(Taxon, "Other")) %>%
  dplyr::mutate(Taxon = factor(Taxon, levels = c("Alistipes", "Bacteroides", "COE1", "Eisenbergiella","Enterocloster", "Flavonifractor", "Parabacteroides", "Phocaeicola", "Ruthenibacterium", "Tolumonas", "Other"))) %>%
  dplyr::mutate(Abundance_10x10 = Abundance/10^10) %>%
  dplyr::mutate(Category2 = ifelse(Category == "Killed", "Killed-CM", "Live-CM")) %>%
  ggplot(aes(x=factor(MouseID, levels = taxabarplot_levels), y=Abundance_10x10, fill=Taxon)) +
  geom_bar(stat="identity") +
  theme_classic(base_size = 10) + 
  scale_fill_manual(values = c("#000000", "#b6dbff","#920000", "#009292", "#ffb6db", "#ffff6d", "#db6d00", "#006ddb", "#24ff24", "#ff6db6", "#004949"), 
                    name = "Genus") +
  theme(axis.text.x=element_blank(),
        axis.ticks.x=element_blank()) +
  coord_cartesian(clip = "off") +
  theme(legend.position="right", legend.key.size=unit(0.4, "cm"),
        legend.text=element_text(face = "italic")) +
  ggh4x::facet_grid2(Sex~Category2, scales = "free_x", independent = "x") +
  guides(fill=guide_legend(title.position = "top")) +
  labs(y= expression(paste("Microbes (GE/g) x", e^10)), x ="Mice") +
    theme(strip.background.y = element_blank(),
        strip.text.y = element_blank()) 

Warning: Expected 2 pieces. Missing pieces filled with `NA` in 182 rows [11, 22, 33, 44, 55, 66, 77, 88, 99, 110, 121, 132, 143, 154, 165, 176, 187, 198, 209, 220, ...].

FigS4_Taxa_barplot

alpha and beta diversity in qiime with absolute sequences

gc()
# prepare file
qiime2_reads_rare_norm<- reads_rare_norm %>%
  dplyr::select(sampleid, contains("s__")) %>%
  dplyr::rename(rowname = sampleid) %>% 
  tidyr::gather(var, value, -rowname) %>% 
  tidyr::spread(rowname, value) %>%
  dplyr::group_by(var) %>%
  dplyr::mutate(sum_abubnd = sum(c_across(where(is.numeric)))) %>%
  dplyr::filter(sum_abubnd > 0) %>%
  dplyr::select(-sum_abubnd) %>%
  dplyr::ungroup() %>%
  dplyr::left_join(Taxonomy, by ="var") %>%
  dplyr::select(contains("OTU"), contains("CC"), taxonomy) %>%
  dplyr::rename("#OTU ID" = OTU_ID)
gc()
#save output
write.table(qiime2_reads_rare_norm, file = "./data/Interim_output/qiime2_reads_rare_norm.txt", sep = "\t" , row.names = FALSE, quote = FALSE)

source activate /ebio/abt3_projects/Christensenella_CM20/envs/qiime2 #path to qiime2-2022.2 environment

##qiime import data
biom convert -i ./data/Interim_output/qiime2_reads_rare_norm.txt \
          -o ./data/Interim_output/qiime2_reads_rare_norm.biom \
          --to-hdf5 --process-obs-metadata taxonomy \
          --table-type "OTU table"

qiime tools import \
          --input-path ./data/Interim_output/qiime2_reads_rare_norm.biom \
          --output-path ./data/Interim_output/qiime2_reads_rare_norm.qza \
          --type 'FeatureTable[Frequency]' \
          --input-format BIOMV210Format

##run diversity
qiime diversity alpha \
          --p-metric observed_features \
          --i-table ./data/Interim_output/qiime2_reads_rare_norm.qza \
          --o-alpha-diversity ./data/Interim_output/alpha_observed_features.qza

qiime diversity alpha \
          --p-metric shannon \
          --i-table ./data/Interim_output/qiime2_reads_rare_norm.qza \
          --o-alpha-diversity ./data/Interim_output/alpha_shannon.qza

qiime diversity alpha \
          --p-metric pielou_e \
          --i-table ./data/Interim_output/qiime2_reads_rare_norm.qza \
          --o-alpha-diversity ./data/Interim_output/alpha_pielou.qza
          
qiime diversity alpha-phylogenetic \
          --p-metric faith_pd \
          --i-table ./data/Interim_output/qiime2_reads_rare_norm.qza \
          --i-phylogeny ./data/Metagenomics/rooted_tree.qza \
          --o-alpha-diversity ./data/Interim_output/alpha_faithPD.qza


qiime diversity beta-phylogenetic \
          --p-metric weighted_unifrac \
          --i-table ./data/Interim_output/qiime2_reads_rare_norm.qza \
          --i-phylogeny ./data/Metagenomics/rooted_tree.qza \
          --o-distance-matrix ./data/Interim_output/beta_wUF.qza

qiime diversity beta-phylogenetic \
          --p-metric unweighted_unifrac \
          --i-table ./data/Interim_output/qiime2_reads_rare_norm.qza \
          --i-phylogeny ./data/Metagenomics/rooted_tree.qza \
          --o-distance-matrix ./data/Interim_output/beta_uwUF.qza

##export data
qiime tools export \
  --input-path ./data/Interim_output/alpha_observed_features.qza \
  --output-path ./data/Interim_output/alpha_observed_features

qiime tools export \
  --input-path ./data/Interim_output/alpha_shannon.qza \
  --output-path ./data/Interim_output/alpha_shannon

qiime tools export \
  --input-path ./data/Interim_output/alpha_pielou.qza \
  --output-path ./data/Interim_output/alpha_pielou

qiime tools export \
  --input-path ./data/Interim_output/alpha_faithPD.qza \
  --output-path ./data/Interim_output/alpha_faithPD

qiime tools export \
  --input-path ./data/Interim_output/beta_wUF.qza \
  --output-path ./data/Interim_output/beta_wUF

qiime tools export \
  --input-path ./data/Interim_output/beta_uwUF.qza \
  --output-path ./data/Interim_output/beta_uwUF

alpha diversity

Alpha_FPD <- fread("./data/Interim_output/alpha_faithPD/alpha-diversity.tsv") %>%
  dplyr::rename(sampleid = "#SampleID")
Alpha_observedF <- fread("./data/Interim_output/alpha_observed_features/alpha-diversity.tsv") %>%
  dplyr::rename(sampleid = "V1")
Alpha_S <- fread("./data/Interim_output/alpha_shannon/alpha-diversity.tsv") %>%
  dplyr::rename(sampleid = "V1")
Alpha_P <- fread("./data/Interim_output/alpha_pielou/alpha-diversity.tsv") %>%
  dplyr::rename(sampleid = "V1")
  
Alpha_div <- Alpha_FPD %>%
  dplyr::left_join(Alpha_observedF, by = "sampleid") %>%
  dplyr::left_join(Alpha_S, by = "sampleid") %>%
  dplyr::left_join(Alpha_P, by = "sampleid") %>%
  dplyr::left_join(Metagenomics_metadata_CC, by = "sampleid") %>%
  dplyr::filter(!grepl(paste(Metagenomics_exclude_id, collapse="|"), sampleid)) %>%
  dplyr::left_join(metadata, by = "MouseID")

Fig3_SE <- Alpha_div %>%
  ggplot(aes(x = Category, y = shannon_entropy, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(legend.position="right") +
  labs(y = "Shannon Entropy") +
  theme(axis.text.x = element_text(face = "italic")) +
  theme(axis.title.x=element_blank())+
  facet_grid(.~Sex)
Fig3_SE

Fig3_OF <- Alpha_div %>%
  ggplot(aes(x = Category, y = observed_features, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(legend.position="right") +
  labs(y = "# microbial species") +
  theme(axis.text.x = element_text(face = "italic")) +
  theme(axis.title.x=element_blank())+
  facet_grid(.~Sex)
Fig3_OF

FigS4_FDP  <- Alpha_div %>%
  ggplot(aes(x = Category, y = faith_pd, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(legend.position="right") +
  labs(y = "Faith's PD") +
  theme(axis.text.x = element_text(face = "italic")) +
  theme(axis.title.x=element_blank()) +
  facet_grid(.~Sex)
FigS4_FDP 

FigS4_PE  <- Alpha_div %>%
  ggplot(aes(x = Category, y = pielou_evenness, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(legend.position="right") +
  labs(y = "Pilou evenness") +
  theme(axis.text.x = element_text(face = "italic")) +
  theme(axis.title.x=element_blank())+
  facet_grid(.~Sex)
FigS4_PE 

wilcox.test due to problems with normality

options(backup_options)
Alpha_div_M <- Alpha_div %>%
  dplyr::filter(Sex =="M")
Alpha_div_F <- Alpha_div %>%
  dplyr::filter(Sex =="F")

print("se M")

[1] "se M"

wilcox.test(shannon_entropy~Category, data = Alpha_div_M, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  shannon_entropy by Category
W = 1341, p-value = 0.003972
alternative hypothesis: true location shift is not equal to 0

print("se F")

[1] "se F"

wilcox.test(shannon_entropy~Category, data = Alpha_div_F, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  shannon_entropy by Category
W = 1191, p-value = 0.398
alternative hypothesis: true location shift is not equal to 0

print("pe M")

[1] "pe M"

wilcox.test(pielou_evenness~Category, data = Alpha_div_M, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  pielou_evenness by Category
W = 1143, p-value = 0.2093
alternative hypothesis: true location shift is not equal to 0

print("pe F")

[1] "pe F"

wilcox.test(pielou_evenness~Category, data = Alpha_div_F, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  pielou_evenness by Category
W = 1231, p-value = 0.2491
alternative hypothesis: true location shift is not equal to 0

print("fpd M")

[1] "fpd M"

wilcox.test(faith_pd~Category, data = Alpha_div_M, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  faith_pd by Category
W = 1260, p-value = 0.02672
alternative hypothesis: true location shift is not equal to 0

print("fpd F")

[1] "fpd F"

wilcox.test(faith_pd~Category, data = Alpha_div_F, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  faith_pd by Category
W = 1147, p-value = 0.612
alternative hypothesis: true location shift is not equal to 0

print("of M")

[1] "of M"

wilcox.test(observed_features~Category, data = Alpha_div_M, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  observed_features by Category
W = 1293, p-value = 0.0129
alternative hypothesis: true location shift is not equal to 0

print("of F")

[1] "of F"

wilcox.test(observed_features~Category, data = Alpha_div_F, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  observed_features by Category
W = 1122.5, p-value = 0.7498
alternative hypothesis: true location shift is not equal to 0

standard_error <- function(x) sd(x) / sqrt(length(x))

mean_se_treat_M <- Alpha_div_M %>%
  dplyr::group_by(Category) %>%
  dplyr::summarise(mean = mean(shannon_entropy), se = standard_error(shannon_entropy))%>%
  dplyr::mutate(alpha = "se")

mean_pe_treat_M <- Alpha_div_M %>%
  dplyr::group_by(Category) %>%
  dplyr::summarise(mean = mean(pielou_evenness), se = standard_error(pielou_evenness))%>%
  dplyr::mutate(alpha = "pe")

mean_fdp_treat_M <- Alpha_div_M%>%
  dplyr::group_by(Category) %>%
  dplyr::summarise(mean = mean(faith_pd), se = standard_error(faith_pd))%>%
  dplyr::mutate(alpha = "fdp")

mean_of_treat_M <- Alpha_div_M %>%
  dplyr::group_by(Category) %>%
  dplyr::summarise(mean = mean(observed_features), se = standard_error(observed_features)) %>%
  dplyr::mutate(alpha = "of")

rbind(mean_se_treat_M, mean_pe_treat_M, mean_fdp_treat_M, mean_of_treat_M)

mean_se_treat_F <- Alpha_div_F %>%
  dplyr::group_by(Category) %>%
  dplyr::summarise(mean = mean(shannon_entropy), se = standard_error(shannon_entropy))%>%
  dplyr::mutate(alpha = "se")

mean_pe_treat_F <- Alpha_div_F %>%
  dplyr::group_by(Category) %>%
  dplyr::summarise(mean = mean(pielou_evenness), se = standard_error(pielou_evenness))%>%
  dplyr::mutate(alpha = "pe")

mean_fdp_treat_F<- Alpha_div_F%>%
  dplyr::group_by(Category) %>%
  dplyr::summarise(mean = mean(faith_pd), se = standard_error(faith_pd))%>%
  dplyr::mutate(alpha = "fdp")

mean_of_treat_F <- Alpha_div_F %>%
  dplyr::group_by(Category) %>%
  dplyr::summarise(mean = mean(observed_features), se = standard_error(observed_features)) %>%
  dplyr::mutate(alpha = "of")

rbind(mean_se_treat_F, mean_pe_treat_F, mean_fdp_treat_F, mean_of_treat_F)

beta diversity

Beta_wUF <- fread("./data/Interim_output/beta_wUF/distance-matrix.tsv")
Beta_uwUF <- fread("./data/Interim_output/beta_uwUF/distance-matrix.tsv")

Beta_wUF.2 <- Beta_wUF %>%
  dplyr::filter(!grepl(paste(Metagenomics_exclude_id, collapse="|"), V1)) %>%
  dplyr::select(-contains(Metagenomics_exclude_id)) %>%
  dplyr::arrange(V1) %>%
  dplyr::select(order(colnames(.))) %>%
  tibble::column_to_rownames(var = "V1")

Beta_uwUF.2 <- Beta_uwUF %>%
  dplyr::filter(!grepl(paste(Metagenomics_exclude_id, collapse="|"), V1)) %>%
  dplyr::select(-contains(Metagenomics_exclude_id)) %>%
  dplyr::arrange(V1) %>%
  dplyr::select(order(colnames(.))) %>%
  tibble::column_to_rownames(var = "V1")

reads_rare_norm_metadata <- reads_rare_norm_metadata[ order(row.names(reads_rare_norm_metadata)), ] 

reads_rare_norm_metadata.melt <- reads_rare_norm_metadata %>% 
  tibble::rownames_to_column(var = "sampleid")  %>%
  dplyr::select(sampleid, Category, Sex) %>%
    mutate_if(is.factor,as.character)

wUF_melt <- melt(as.matrix(Beta_wUF.2)) %>%
    filter(as.character(Var1) != as.character(Var2)) %>%
    mutate_if(is.factor,as.character)

uwUF_melt <- melt(as.matrix(Beta_uwUF.2)) %>%
    filter(as.character(Var1) != as.character(Var2)) %>%
    mutate_if(is.factor,as.character)

colnames(reads_rare_norm_metadata.melt) = c("Var1", "Category1", "Sex1")
wUF_melt = left_join(wUF_melt, reads_rare_norm_metadata.melt, by = "Var1")
uwUF_melt = left_join(uwUF_melt, reads_rare_norm_metadata.melt, by = "Var1")

colnames(reads_rare_norm_metadata.melt) = c("Var2", "Category2", "Sex2")
wUF_melt = left_join(wUF_melt, reads_rare_norm_metadata.melt, by = "Var2")
uwUF_melt = left_join(uwUF_melt, reads_rare_norm_metadata.melt, by = "Var2")

set.seed(765)

wUF_melt.2 <- wUF_melt %>%
  dplyr::filter(Category1 == Category2, Sex1 == Sex2) %>%
  dplyr::mutate(Category = Category1, Sex = Sex1)
# Issue cant use linaer models --> no normality in residuals 
# I will use wilcoxen rank sum test
wUF_melt_M<- wUF_melt.2 %>%
  dplyr::filter(Sex =="M")
wUF_melt_F<- wUF_melt.2 %>%
  dplyr::filter(Sex =="F") 

print("wUF M")

[1] "wUF M"

wilcox.test(value ~ Category, data = wUF_melt_M, exact = FALSE, correct = FALSE)

    Wilcoxon rank sum test

data:  value by Category
W = 2395224, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0

print("wUF F")

[1] "wUF F"

wilcox.test(value ~ Category, data = wUF_melt_F, exact = FALSE, correct = FALSE)

    Wilcoxon rank sum test

data:  value by Category
W = 2216996, p-value = 0.6028
alternative hypothesis: true location shift is not equal to 0

uwUF_melt.2 <- uwUF_melt %>%
  dplyr::filter(Category1 == Category2, Sex1 == Sex2) %>%
  dplyr::mutate(Category = Category1, Sex = Sex1)
# Issue cant use linaer models --> no normality in residuals 
# I will use wilcoxen rank sum test
uwUF_melt_M<- uwUF_melt.2 %>%
  dplyr::filter(Sex =="M") 
uwUF_melt_F<- uwUF_melt.2 %>%
  dplyr::filter(Sex =="F") 

print("uwUF M")

[1] "uwUF M"

wilcox.test(value ~ Category, data = uwUF_melt_M, exact = FALSE, correct = FALSE)

    Wilcoxon rank sum test

data:  value by Category
W = 2534932, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0

print("uwUF F")

[1] "uwUF F"

 wilcox.test(value ~ Category, data = uwUF_melt_F, exact = FALSE, correct = FALSE) 

    Wilcoxon rank sum test

data:  value by Category
W = 2395228, p-value = 7.325e-05
alternative hypothesis: true location shift is not equal to 0

Fig3_wUF <- wUF_melt.2 %>%
  ggplot(aes(x = Category, y = value, fill=factor(Category))) +
  theme_classic(base_size = 10) +
  geom_boxplot(alpha =0.7, outlier.size = 0.3, outlier.alpha = 0.5) +
  #facet_grid(~ Sex, scales = "free") +
  scale_fill_manual(values=color_Category )+
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  labs(y = "weighted\nUniFrac distances")  +
  theme(axis.text.x = element_text(face = "italic"), axis.title.x=element_blank())+
  facet_grid(.~Sex)
Fig3_wUF

Fig3_uwUF <-uwUF_melt.2 %>%
  ggplot(aes(x = Category, y = value, fill=factor(Category))) +
  theme_classic(base_size = 10) +
  geom_boxplot(alpha =0.7, outlier.size = 0.3, outlier.alpha = 0.5) +
  #facet_grid(~ Sex, scales = "free") +
  scale_fill_manual(values=color_Category )+
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  labs(y = "unweighted\nUniFrac distances")  +
  theme(axis.text.x = element_text(face = "italic"), axis.title.x=element_blank())+
  facet_grid(.~Sex)
Fig3_uwUF

Differential Abundance Analysis

gc()

            used  (Mb) gc trigger   (Mb)  max used   (Mb)
Ncells  18538619 990.1   41356865 2208.7  41356865 2208.7
Vcells 119645453 912.9  222647753 1698.7 222647753 1698.7

# median abundance C. minuta = 0.000101087 -> 0.01% 
thresh_median <- 0.00001 #threshold median abundance (= median abundance C. minuta / 10)

reads_rare.2 <- reads_rare %>%
  tibble::rownames_to_column(var = "rowname") %>% 
  tidyr::gather(var, value, -rowname) %>% 
  tidyr::spread(rowname, value) 

reads_rare.2[,2:183] <- as.data.table(lapply(reads_rare.2[,2:183], as.numeric))


reads_rare_F<-reads_rare.2 %>%
  dplyr::select(var, contains("fB")) %>%
  dplyr::rename(rowname = var) %>%
  dplyr::mutate_if(is.numeric,function(x) (x/sum(x))) %>%
  dplyr::rowwise() %>%
  dplyr::mutate(median_abund = median(c_across(where(is.numeric)))) %>%
  dplyr::filter(median_abund >= thresh_median) %>%
  dplyr::select(-median_abund) %>%
  tidyr::gather(var, value, -rowname) %>% 
  tidyr::spread(rowname, value) %>% 
  dplyr::rename(sampleid = var) %>%
  dplyr::left_join(Metagenomics_metadata_CC, by ="sampleid") %>%
  dplyr::left_join(metadata, by ="MouseID") %>%
  dplyr::left_join(qPCR_data, by ="MouseID") %>%
  dplyr::mutate(qPCR_16S_SQ_per_gCC = as.numeric(qPCR_16S_SQ_per_gCC))  %>%
  dplyr::group_by(sampleid) %>%
  dplyr::mutate_each(funs(.*qPCR_16S_SQ_per_gCC), starts_with("s__")) %>%
  dplyr::ungroup() 

Maaslin_metadata_F <- reads_rare_F %>%
  dplyr::select(-contains("s__")) %>% 
  tibble::column_to_rownames(var = "sampleid")

Maaslin_reads_F <- reads_rare_F %>%
  dplyr::select(sampleid, contains("s__")) %>%
  tidyr::gather(var, value, -sampleid) %>% 
  tidyr::spread(sampleid, value) %>%
  dplyr::mutate_if(is.numeric, round) %>% 
  tibble::column_to_rownames(var = "var")

reads_rare_M<-reads_rare.2 %>%
  dplyr::select(var, contains("mB")) %>%
  dplyr::rename(rowname = var) %>%
  dplyr::mutate_if(is.numeric,function(x) (x/sum(x))) %>%
  dplyr::rowwise() %>%
  dplyr::mutate(median_abund = median(c_across(where(is.numeric)))) %>%
  dplyr::filter(median_abund >= thresh_median) %>%
  dplyr::select(-median_abund) %>%
  tidyr::gather(var, value, -rowname) %>% 
  tidyr::spread(rowname, value) %>% 
  dplyr::rename(sampleid = var) %>%
  dplyr::left_join(Metagenomics_metadata_CC, by ="sampleid") %>%
  dplyr::left_join(metadata, by ="MouseID") %>%
  dplyr::left_join(qPCR_data, by ="MouseID") %>%
  dplyr::mutate(qPCR_16S_SQ_per_gCC = as.numeric(qPCR_16S_SQ_per_gCC))  %>%
  dplyr::group_by(sampleid) %>%
  dplyr::mutate_each(funs(.*qPCR_16S_SQ_per_gCC), starts_with("s__")) %>%
  dplyr::ungroup()

Maaslin_metadata_M  <- reads_rare_M %>%
  dplyr::select(-contains("s__")) %>% 
  tibble::column_to_rownames(var = "sampleid")

Maaslin_reads_M <- reads_rare_M %>%
  dplyr::select(sampleid, contains("s__")) %>%
  tidyr::gather(var, value, -sampleid) %>% 
  tidyr::spread(sampleid, value) %>%
  dplyr::mutate_if(is.numeric, round) %>% 
  tibble::column_to_rownames(var = "var")

gc()
options(backup_options)

CM20_Maaslin_species_F_treat = Maaslin2(
    input_data = Maaslin_reads_F, 
    input_metadata = Maaslin_metadata_F, 
    output = "./data/Interim_output/Maaslin_species_F_treat", 
        fixed_effects = c("Category", "Run"), 
    random_effects = c("Batch"),
    reference= c("Run,R1-Hiseq"),
    min_prevalence = 0.5, 
    max_significance = 0.1, 
    plot_scatter = FALSE, 
    plot_heatmap = FALSE)

CM20_Maaslin_species_M_treat = Maaslin2(
    input_data = Maaslin_reads_M, 
    input_metadata = Maaslin_metadata_M, 
    output = "./data/Interim_output/Maaslin_species_M_treat", 
        fixed_effects = c("Category", "Run"), 
    random_effects = c("Batch"),
    reference= c("Run,R1-Hiseq"),
    min_prevalence = 0.5, 
    max_significance = 0.1, 
    plot_scatter = FALSE, 
    plot_heatmap = FALSE)

Results_MaasLin_F <- fread("./data/Interim_output/Maaslin_species_F_treat/all_results.tsv") %>%
  dplyr::filter(metadata == "Category") %>%
  dplyr::mutate(qval = p.adjust(pval, method = "BH")) %>%
  dplyr::arrange(qval) %>% 
  dplyr::mutate(analysis = "F")
Results_MaasLin_M <- fread("./data/Interim_output/Maaslin_species_M_treat/all_results.tsv") %>%
  dplyr::filter(metadata == "Category") %>%
  dplyr::mutate(qval = p.adjust(pval, method = "BH")) %>%
  dplyr::arrange(qval) %>% 
  dplyr::mutate(analysis = "M")

sig_species_treat_F <- Results_MaasLin_F %>%
  dplyr::filter(qval <= 0.1 & abs(coef) >= 1) %>%
  pull(feature)
sig_species_treat_M <- Results_MaasLin_M %>%
  dplyr::filter(qval <= 0.1 & abs(coef) >= 1) %>%
  pull(feature)

sig_species_treat_combined <- unique(c(sig_species_treat_F, sig_species_treat_M)) 

Results_MaasLin_Category_species_combined <- rbind(Results_MaasLin_F, Results_MaasLin_M) %>%
  dplyr::filter(grepl(paste(sig_species_treat_combined, collapse="|"), feature)) %>%
  dplyr::rename(var = feature) %>%
  dplyr::mutate(var = properNames(var)) %>%
  dplyr::left_join(Taxonomy, by ="var") %>%
  tidyr::separate(taxonomy, into = tax_levels, sep =";") %>%
  dplyr::select(-var, -OTU_ID) %>%
  dplyr::mutate(Species = sub("s__", "", Species)) %>%
  dplyr::mutate(Family = sub("f__", "", Family)) %>%
  dplyr::mutate(stars = cut(qval, breaks=c(-Inf, 0.001, 0.01, 0.1, Inf), label=c("***", "**", "*", "")) )

Fig3_Maaslin.1<- Results_MaasLin_Category_species_combined %>% 
  arrange(desc(Family), desc(Species)) %>%
  mutate(Species = fct_inorder(factor(Species))) %>% 
  ggplot(aes(x=analysis, y=Species, fill=coef), show.legend = T) + 
  geom_tile() + 
  scale_fill_gradient2(low="#D7191C", mid="white", high="#2C7BB6") + 
  geom_text(aes(label=stars), color = "black", size=3) + 
  labs(y=NULL, x=NULL, fill="coef") + 
  theme_classic(base_size = 10) +
  theme(legend.position = 'top')+
  theme(axis.text.y = element_text(face = "italic")) +
  theme(legend.key.height = unit(0.4, 'cm'), legend.key.width = unit(0.4, 'cm')) +
  scale_y_discrete(position = "right")

Fig3_Maaslin.2 <-  Results_MaasLin_Category_species_combined %>%
  arrange(desc(Family), desc(Species)) %>%
  mutate(Species = fct_inorder(factor(Species))) %>% 
  ggplot(aes(y=Species, x=0.1, fill = Family)) +
  scale_fill_manual(values = tableau_color_pal('Tableau 20')(19)) +
  geom_tile() +
  theme_void(base_size = 10) +
  theme(legend.position = 'none')

Fig3_Maaslin.legend <- cowplot::get_legend(Results_MaasLin_Category_species_combined %>%
  arrange(desc(Family), desc(Species)) %>%
  mutate(Species = fct_inorder(factor(Species))) %>% 
  ggplot(aes(y=Species, x=0.1, fill = Family)) +
  scale_fill_manual(values = tableau_color_pal('Tableau 20')(19)) +
  geom_tile() +
  theme_classic(base_size = 10) +
  theme(legend.position = 'left') +
  guides(fill=guide_legend(nrow=9, title.position="top")) + 
  theme(legend.key.size = unit(0.4, 'cm'), 
        legend.text=element_text(face = "italic")) +
  guides(fill=guide_legend(nrow=5,byrow=TRUE)))

Fig3_Maaslin <- Fig3_Maaslin.2 + Fig3_Maaslin.1  + plot_layout(widths = c(0.3, 1))

Fig3_Maaslin

Figure 3 - C. minuta effects on gut microbiome biomass and diversity.

Fig3_legend_treatment_sex <- cowplot::get_legend(metadata_Cmin %>% 
  dplyr::mutate(Sex = ifelse(Sex == "F", "Female", "Male"), 
                Category = ifelse(Category == "Killed", "Killed-CM", "Live-CM"), 
                Sex_Category = paste(Sex, Category, sep = " ")) %>%
  ggplot(aes(x = Category, y = qPCR_Cmin_per_gCC, shape = Sex_Category, color = Sex_Category)) + 
  geom_point(aes(shape = Sex_Category, color= Sex_Category), alpha =0.7, size =0.5, show.legend = T) +
  scale_shape_manual(values=c(16, 16,4, 4)) + 
  scale_color_manual(values=c(color_Category, color_Category)) +
  theme_classic(base_size = 10) +
  theme(legend.title=element_blank()) + 
  guides(shape=guide_legend(nrow=2, byrow=TRUE, 
                            override.aes = list(size = 3, alpha = 1))))

Fig3_align <- cowplot::align_plots(Fig3_qPCR_bacteria + theme(legend.position="none"),
                                       Fig3_Cmin_biomass + theme(legend.position="none"),
                                       Fig3_OF + theme(legend.position="none"),
                                       Fig3_SE + theme(legend.position="none"),
                                       Fig3_wUF + theme(legend.position="none"),
                                       Fig3_uwUF + theme(legend.position="none"),
                                       align = 'hv', axis = 'rlbt')

`geom_smooth()` using formula 'y ~ x'

Fig3_1 <- cowplot::plot_grid(Fig3_align[[1]],
                               Fig3_align[[2]],
                               Fig3_align[[3]],
                               Fig3_align[[4]],
                               Fig3_align[[5]],
                               Fig3_align[[6]],
                               ncol=2, nrow=3, labels = c( "A", "B", "C", "D", "E", "F"), label_size = 12, rel_widths = c(1, 1), rel_heights = c(1, 1, 1))

Fig3_2 <- cowplot::plot_grid(Fig3_legend_treatment_sex,
                               Fig3_Maaslin,
                               Fig3_Maaslin.legend,
                               ncol=1, nrow=3, labels = c(NA, "G", NA), label_size = 12, rel_heights = c(0.2, 1.5, 0.5))

Figure3 <- cowplot::plot_grid(Fig3_1, 
                               Fig3_2, 
                               ncol=2, nrow=1, rel_widths = c(1.3, 1))

Warning: Removed 1 rows containing missing values (geom_text).Warning: Removed 1 rows containing missing values (geom_text).

Figure3

ggsave("./Figure3.pdf", plot = Figure3, device = cairo_pdf,  
  width = 17 ,
  height = 14,
  units = "cm")

Figure S4 - C. minuta amendment resulted in lower complexity in males.

FigS4_barplot_legend <- cowplot::get_legend(FigS4_Taxa_barplot + theme(legend.position="bottom") )
FigS4_legend_treatment_sex <- cowplot::get_legend(metadata_Cmin %>% 
  dplyr::mutate(Sex = ifelse(Sex == "F", "Female", "Male"), 
                Category = ifelse(Category == "Killed", "Killed-CM", "Live-CM"), 
                Sex_Category = paste(Sex, Category, sep = " ")) %>%
  ggplot(aes(x = Category, y = qPCR_Cmin_per_gCC, shape = Sex_Category, color = Sex_Category)) + 
  geom_point(aes(shape = Sex_Category, color= Sex_Category), alpha =0.7, size =0.5, show.legend = T) +
  scale_shape_manual(values=c(16, 16,4, 4)) + 
  scale_color_manual(values=c(color_Category, color_Category)) +
  theme_classic(base_size = 10) +
  theme(legend.title=element_blank()) + 
  guides(shape=guide_legend(override.aes = list(size = 3, alpha = 1))) +
  theme(legend.position="bottom"))

FigS4_align <- cowplot::align_plots(FigS4_FDP + theme(legend.position="none"),
                                          FigS4_PE + theme(legend.position="none"),
                                          align = 'v', axis = 'l')

FigS4_1 <- cowplot::plot_grid(NA, 
                                  FigS4_Taxa_barplot + theme(legend.position="none"),
                                  ncol=2, nrow=1, rel_widths = c(0.1, 2),  labels = c( "A", NA), label_size = 12)

Warning: Cannot convert object of class logical into a grob.

FigS4_2 <- cowplot::plot_grid(FigS4_align[[1]],
                                    FigS4_align[[2]],
                                    ncol=2, nrow=1, rel_widths = c(1, 1),  labels = c("B","C"), label_size = 12)


FigureS4 <- cowplot::plot_grid(FigS4_1, 
                                  FigS4_barplot_legend,
                                    FigS4_2, 
                                  FigS4_legend_treatment_sex,
                                    ncol=1, nrow=4, rel_heights = c(1.6, 0.35, 1, 0.2))

Warning: Removed 1 rows containing missing values (geom_text).

FigureS4

ggsave("./FigureS4.pdf", plot = FigureS4, device = cairo_pdf,  
  width = 10.5,
  height = 13,
  units = "cm")

Amendment of C. minuta altered the host metabolome

SCFA

GCMS_SCFAs.2<- GCMS_SCFAs %>%
  dplyr::left_join(metadata, by = "MouseID")

Fig4_totalSCFA <- GCMS_SCFAs.2 %>% 
  ggplot(aes(x = Category, y = Total_SCFA, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y= "Total SCFAs (mM/g)") +
  theme(axis.text.x = element_text(face = "italic")) +
  facet_grid(.~Sex) +
  theme(strip.background = element_blank(),strip.text = element_blank()) 
Fig4_totalSCFA

set.seed(501)
GCMS_SCFAs.2$Total_SCFA_norm <- predict(bestNormalize(GCMS_SCFAs.2$Total_SCFA))

options(contrasts=c("contr.sum", "contr.poly"))
model_scfa<- lmer(Total_SCFA_norm ~ Category*Sex + Extraction_round + (Category|Sex/Batch) + (Category|Batch) + (1|Deriv), data = GCMS_SCFAs.2)

boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -2.1e-07Warning: Model may not have converged with 1 eigenvalue close to zero: 3.5e-09

step(model_scfa)

boundary (singular) fit: see help('isSingular')
Warning: Model may not have converged with 2 eigenvalues close to zero: 3.1e-09 -1.4e-09boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -8.6e-04Warning: Model may not have converged with 1 eigenvalue close to zero: -6.8e-09boundary (singular) fit: see help('isSingular')
Warning: Model may not have converged with 2 eigenvalues close to zero: 3.1e-09 -1.4e-09boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -1.0e-06Warning: Model may not have converged with 2 eigenvalues close to zero: 8.6e-09 -2.3e-09boundary (singular) fit: see help('isSingular')
Warning: Model may not have converged with 2 eigenvalues close to zero: 3.1e-09 -1.4e-09boundary (singular) fit: see help('isSingular')
Warning: Model may not have converged with 1 eigenvalue close to zero: -8.4e-09boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -3.3e-03boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -9.4e-08Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model may not have converged with 1 eigenvalue close to zero: 9.7e-11boundary (singular) fit: see help('isSingular')
Warning: Model may not have converged with 1 eigenvalue close to zero: -8.4e-09boundary (singular) fit: see help('isSingular')
Warning: Model may not have converged with 1 eigenvalue close to zero: 8.1e-09boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model may not have converged with 1 eigenvalue close to zero: -9.8e-10boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model may not have converged with 1 eigenvalue close to zero: -9.8e-10boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -2.3e-08boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 2 negative eigenvalues: -2.3e-08 -5.7e+01boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                                   Eliminated npar  logLik    AIC     LRT Df Pr(>Chisq)
<none>                                          17 -233.52 501.05                      
Category in (Category | Batch:Sex)          1   15 -233.52 497.05 0.00000  2     1.0000
Category in (Category | Sex)                2   13 -233.52 493.05 0.00000  2     1.0000
(1 | Batch:Sex)                             3   12 -233.52 491.05 0.00000  1     1.0000
(1 | Sex)                                   4   11 -233.52 489.05 0.00000  1     0.9999
Category in (Category | Batch)              5    9 -234.54 487.07 2.02774  2     0.3628
(1 | Batch)                                 6    8 -234.54 485.07 0.00000  1     1.0000
(1 | Deriv)                                 7    7 -234.87 483.73 0.65658  1     0.4178

Backward reduced fixed-effect table:
                 Eliminated Df Sum of Sq    RSS     AIC F value    Pr(>F)    
Extraction_round          0  2    51.800 176.15   1.018  36.867 4.124e-14 ***
Category:Sex              0  1     3.638 127.99 -55.434   5.178   0.02407 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Total_SCFA_norm ~ Category + Sex + Extraction_round + Category:Sex

options(contrasts=c("contr.sum", "contr.poly"))
model_scfa.2<- lm(Total_SCFA_norm ~ Category + Sex + Extraction_round + Category:Sex, data = GCMS_SCFAs.2)
shapiro.test(residuals(model_scfa.2))

    Shapiro-Wilk normality test

data:  residuals(model_scfa.2)
W = 0.98942, p-value = 0.1924

anova(lm(residuals(model_scfa.2)~fitted.values(model_scfa.2)))

Analysis of Variance Table

Response: residuals(model_scfa.2)
                             Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_scfa.2)   1   0.00 0.00000       0      1
Residuals                   181 124.35 0.68701               

par(mfrow=c(2,2))
plot(model_scfa.2)

GCMS_SCFAs_M <- GCMS_SCFAs.2 %>% dplyr::filter(Sex =="M")
GCMS_SCFAs_F <- GCMS_SCFAs.2 %>% dplyr::filter(Sex =="F")

model_scfa_m <- lm(Total_SCFA_norm ~ Category + Extraction_round, data = GCMS_SCFAs_M)
model_scfa_f <- lm(Total_SCFA_norm ~ Category + Extraction_round, data = GCMS_SCFAs_F)

shapiro.test(residuals(model_scfa_m))

    Shapiro-Wilk normality test

data:  residuals(model_scfa_m)
W = 0.98536, p-value = 0.4111

shapiro.test(residuals(model_scfa_f))

    Shapiro-Wilk normality test

data:  residuals(model_scfa_f)
W = 0.99216, p-value = 0.8629

anova(lm(residuals(model_scfa_m)~fitted.values(model_scfa_m)))

Analysis of Variance Table

Response: residuals(model_scfa_m)
                            Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_scfa_m)  1  0.000  0.0000       0      1
Residuals                   88 73.013  0.8297               

anova(lm(residuals(model_scfa_f)~fitted.values(model_scfa_f)))

Analysis of Variance Table

Response: residuals(model_scfa_f)
                            Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_scfa_f)  1  0.000 0.00000       0      1
Residuals                   91 49.676 0.54589               

car::Anova(model_scfa_m, test.statistic="F", type = "II")

Anova Table (Type II tests)

Response: Total_SCFA_norm
                 Sum Sq Df F value    Pr(>F)    
Category          1.989  1  2.3426    0.1296    
Extraction_round 19.815  2 11.6696 3.282e-05 ***
Residuals        73.013 86                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

car::Anova(model_scfa_f, test.statistic="F", type = "II")

Anova Table (Type II tests)

Response: Total_SCFA_norm
                 Sum Sq Df F value    Pr(>F)    
Category          1.499  1  2.6851    0.1048    
Extraction_round 33.646  2 30.1403 1.011e-10 ***
Residuals        49.676 89                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model_scfa_meff <- lm(Total_SCFA ~ Category + Extraction_round, data = GCMS_SCFAs_M)
model_scfa_feff <- lm(Total_SCFA ~ Category + Extraction_round, data = GCMS_SCFAs_F)

effects::effect("Category", model_scfa_meff, se=TRUE) %>% as.data.frame

effects::effect("Category", model_scfa_feff, se=TRUE) %>% as.data.frame

SCFA_F <- GCMS_SCFAs.2 %>% 
  dplyr::filter(Sex =="F") %>%
  dplyr::select(MouseID, Acetate:Caproate, Propionate:Valerate) %>%
  tibble::column_to_rownames(var = "MouseID") 

SCFA_M <- GCMS_SCFAs.2 %>% 
  dplyr::filter(Sex =="M") %>%
  dplyr::select(MouseID, Acetate:Caproate, Propionate:Valerate) %>%
  tibble::column_to_rownames(var = "MouseID") 

SCFA_F_meta <- GCMS_SCFAs.2 %>% 
  dplyr::filter(Sex =="F") %>%
  tibble::column_to_rownames(var = "MouseID") 

SCFA_M_meta <- GCMS_SCFAs.2 %>% 
  dplyr::filter(Sex =="M") %>%
  tibble::column_to_rownames(var = "MouseID") 

options(backup_options)

SCFA_F = Maaslin2(
    input_data = SCFA_F, 
    input_metadata = SCFA_F_meta, 
    output = "./data/Interim_output/SCFA_F", 
    fixed_effects = c("Category", "Extraction_round"),
    reference = c("Extraction_round,E1"),
    random_effects = c("Batch"), 
    min_prevalence = FALSE,
    min_abundance = FALSE,
    min_variance = FALSE, 
    analysis_method = "LM", 
    max_significance = 0.1)

SCFA_M = Maaslin2(
    input_data = SCFA_M, 
    input_metadata = SCFA_M_meta, 
    output = "./data/Interim_output/SCFA_M", 
    fixed_effects = c("Category", "Extraction_round"), 
    reference = c("Extraction_round,E1"),
    random_effects = c("Batch"), 
    min_prevalence = FALSE,
    min_abundance = FALSE,
    min_variance = FALSE,
    max_significance = 0.1)

SCFA_F_results <- fread("./data/Interim_output/SCFA_F/all_results.tsv")
SCFA_M_results <-fread("./data/Interim_output/SCFA_M/all_results.tsv")

SCFA_F_treat <- SCFA_F_results %>%
  dplyr::filter(metadata == "Category") %>%
  dplyr::mutate(qval2 = p.adjust(pval, method= "BH")) %>%
  dplyr::arrange(qval2)

SCFA_M_treat <-SCFA_M_results %>%
  dplyr::filter(metadata == "Category") %>%
  dplyr::mutate(qval2 = p.adjust(pval, method= "BH")) %>%
  dplyr::arrange(qval2) 

SCFA_F_treat

SCFA_M_treat

Fig4_butyrate <- GCMS_SCFAs.2 %>%
  ggplot(aes(x = Category, y = Butyrate, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y= "Butyrate (mM/g)") +
  theme(axis.text.x = element_text(face = "italic")) +
  facet_grid(.~Sex) +
  theme(strip.background = element_blank(),strip.text = element_blank()) 
Fig4_butyrate

FigS5_SCFAs <- GCMS_SCFAs.2 %>%
  dplyr::mutate(Sex_Category =paste (Sex, Category, sep ="_")) %>%
  dplyr::select(Category,Sex_Category, Batch, Sex, Acetate, Propionate, Valerate, Caproate) %>%
  tidyr::gather(SCFA, measurement, c(Acetate:Caproate)) %>%
  ggplot(aes(x = Category, y = measurement, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "K", "Live" = "L")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Conc. (mM/g)") +
  theme(axis.title.x=element_blank()) +
  ggh4x::facet_nested_wrap(~ SCFA + Sex, scales ="free_y",ncol =4, labeller=function(x) {x[1]}) +
  theme(legend.position="bottom") +
  theme(axis.text.x = element_text(face = "italic")) +
  theme(strip.background = element_blank())
FigS5_SCFAs

options(contrasts=c("contr.sum", "contr.poly"))

set.seed(193)

SCFA_activity <- Promethion_activity %>%
  dplyr::select(MouseID, Distance_traveled, Beam_breaks, Promethion_Cabinet) %>% 
  dplyr::left_join(GCMS_SCFAs.2, by ="MouseID") %>% 
  drop_na(Distance_traveled, Total_SCFA) %>%
  dplyr::mutate(Distance_traveled_norm = predict(bestNormalize::boxcox(Distance_traveled)), 
                Beam_breaks_norm = predict(bestNormalize(Beam_breaks)), 
                Total_SCFA_norm = predict(bestNormalize(Total_SCFA)), 
                Butyrate_norm = predict(bestNormalize(Butyrate)))

model_scfa_dist <- lmerTest::lmer(Distance_traveled_norm ~ Total_SCFA_norm*Extraction_round*Sex  + (1|Batch), data = SCFA_activity)
lmerTest::step(model_scfa_dist)

Backward reduced random-effect table:

            Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)    
<none>                   14 -228.88 485.76                         
(1 | Batch)          0   13 -251.14 528.29 44.524  1  2.512e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                                     Eliminated Sum Sq Mean Sq NumDF  DenDF F value  Pr(>F)   
Total_SCFA_norm:Extraction_round:Sex          1 0.8478  0.4239     2 162.03  0.7677 0.46576   
Total_SCFA_norm:Sex                           2 0.0004  0.0004     1 164.00  0.0008 0.97749   
Extraction_round:Sex                          3 1.4297  0.7149     2 165.43  1.3066 0.27352   
Total_SCFA_norm:Extraction_round              4 1.8666  0.9333     2 166.98  1.7020 0.18546   
Extraction_round                              5 0.3233  0.1616     2 169.54  0.2923 0.74694   
Total_SCFA_norm                               0 4.9167  4.9167     1 170.97  8.9651 0.00316 **
Sex                                           0 2.9302  2.9302     1  10.07  5.3429 0.04323 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Distance_traveled_norm ~ Total_SCFA_norm + Sex + (1 | Batch)

model_scfa_bb <- lmerTest::lmer(Beam_breaks_norm ~ Total_SCFA_norm*Extraction_round*Sex  + (1|Batch), data = SCFA_activity)
lmerTest::step(model_scfa_bb)

Backward reduced random-effect table:

            Eliminated npar  logLik   AIC    LRT Df Pr(>Chisq)    
<none>                   14 -232.45 492.9                         
(1 | Batch)          0   13 -255.55 537.1 46.201  1  1.067e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                                     Eliminated  Sum Sq Mean Sq NumDF   DenDF F value  Pr(>F)  
Total_SCFA_norm:Extraction_round:Sex          1 0.87087 0.43543     2 161.945  0.7578 0.47033  
Total_SCFA_norm:Sex                           2 0.22280 0.22280     1 163.949  0.3886 0.53390  
Total_SCFA_norm:Extraction_round              3 1.31935 0.65967     2 164.972  1.1550 0.31758  
Extraction_round:Sex                          4 1.37177 0.68589     2 167.475  1.1965 0.30483  
Extraction_round                              5 0.02677 0.01339     2 169.548  0.0233 0.97694  
Sex                                           6 2.76604 2.76604     1  10.085  4.8772 0.05148 .
Total_SCFA_norm                               0 3.12751 3.12751     1 170.725  5.5135 0.02002 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Beam_breaks_norm ~ Total_SCFA_norm + (1 | Batch)

model_ba_dist <- lmerTest::lmer(Distance_traveled_norm ~ Butyrate_norm*Extraction_round*Sex  + (1|Batch), data = SCFA_activity)
lmerTest::step(model_ba_dist)

Backward reduced random-effect table:

            Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)    
<none>                   14 -232.69 493.39                         
(1 | Batch)          0   13 -255.23 536.46 45.071  1  1.901e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                                   Eliminated Sum Sq Mean Sq NumDF   DenDF F value Pr(>F)  
Butyrate_norm:Extraction_round:Sex          1 0.7659  0.3829     2 162.262  0.6660 0.5152  
Butyrate_norm:Extraction_round              2 0.1445  0.0722     2 164.499  0.1262 0.8816  
Butyrate_norm:Sex                           3 0.2885  0.2885     1 163.142  0.5092 0.4765  
Butyrate_norm                               4 0.0787  0.0787     1 174.868  0.1392 0.7095  
Extraction_round:Sex                        5 1.5838  0.7919     2 168.519  1.4065 0.2479  
Extraction_round                            6 2.5251  1.2626     2 170.321  2.2353 0.1101  
Sex                                         0 3.2516  3.2516     1  10.045  5.6720 0.0384 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Distance_traveled_norm ~ Sex + (1 | Batch)

model_ba_bb<- lmerTest::lmer(Beam_breaks_norm ~ Butyrate_norm*Extraction_round*Sex  + (1|Batch), data = SCFA_activity)
lmerTest::step(model_ba_bb)

Backward reduced random-effect table:

            Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)    
<none>                   14 -233.53 495.07                         
(1 | Batch)          0   13 -256.59 539.17 46.101  1  1.123e-11 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                                   Eliminated  Sum Sq Mean Sq NumDF   DenDF F value  Pr(>F)  
Butyrate_norm:Extraction_round:Sex          1 0.64013 0.32007     2 162.243  0.5516 0.57708  
Butyrate_norm:Sex                           2 0.06526 0.06526     1 161.795  0.1130 0.73716  
Extraction_round:Sex                        3 1.40754 0.70377     2 165.761  1.2243 0.29660  
Butyrate_norm:Extraction_round              4 2.55699 1.27849     2 167.457  2.2231 0.11146  
Extraction_round                            5 0.60806 0.30403     2 170.037  0.5207 0.59507  
Butyrate_norm                               6 0.56762 0.56762     1 178.554  0.9773 0.32421  
Sex                                         0 3.01315 3.01315     1  10.062  5.1850 0.04586 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Beam_breaks_norm ~ Sex + (1 | Batch)

-> Only total SCFA correlate to activity. Butyrate doesnt.

options(contrasts=c("contr.sum", "contr.poly"))

model_scfa_dis.2 <- lmerTest::lmer(Distance_traveled_norm ~ Total_SCFA_norm + Sex + (1 | Batch), data = SCFA_activity)
shapiro.test(residuals(model_scfa_dis.2))

    Shapiro-Wilk normality test

data:  residuals(model_scfa_dis.2)
W = 0.99107, p-value = 0.3196

anova(lm(residuals(model_scfa_dis.2)~fitted.values(model_scfa_dis.2)))

Analysis of Variance Table

Response: residuals(model_scfa_dis.2)
                                 Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_scfa_dis.2)   1  0.308 0.30791  0.5962  0.441
Residuals                       180 92.962 0.51646               

model_scfa_bb.2 <- lmerTest::lmer(Beam_breaks_norm ~ Total_SCFA_norm + Sex + (1 | Batch), data = SCFA_activity)
shapiro.test(residuals(model_scfa_bb.2))

    Shapiro-Wilk normality test

data:  residuals(model_scfa_bb.2)
W = 0.99543, p-value = 0.8563

anova(lm(residuals(model_scfa_bb.2)~fitted.values(model_scfa_bb.2)))

Analysis of Variance Table

Response: residuals(model_scfa_bb.2)
                                Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_scfa_bb.2)   1  0.345 0.34492   0.646 0.4226
Residuals                      180 96.106 0.53392               

model_ba_dist.2 <- lmerTest::lmer(Distance_traveled_norm ~ Butyrate_norm + Sex + (1 | Batch), data = SCFA_activity)
shapiro.test(residuals(model_ba_dist.2))

    Shapiro-Wilk normality test

data:  residuals(model_ba_dist.2)
W = 0.99184, p-value = 0.3966

anova(lm(residuals(model_ba_dist.2)~fitted.values(model_ba_dist.2)))

Analysis of Variance Table

Response: residuals(model_ba_dist.2)
                                Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_ba_dist.2)   1  0.351 0.35117  0.6529 0.4201
Residuals                      180 96.810 0.53783               

model_ba_bb.2 <- lmerTest::lmer(Beam_breaks_norm ~ Butyrate_norm + Sex + (1 | Batch), data = SCFA_activity)
shapiro.test(residuals(model_ba_bb.2))

    Shapiro-Wilk normality test

data:  residuals(model_ba_bb.2)
W = 0.99259, p-value = 0.4834

anova(lm(residuals(model_ba_bb.2)~fitted.values(model_ba_bb.2)))

Analysis of Variance Table

Response: residuals(model_ba_bb.2)
                              Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_ba_bb.2)   1  0.371 0.37139  0.6792  0.411
Residuals                    180 98.427 0.54682               

plot(model_scfa_dis.2)

plot(model_scfa_bb.2)

plot(model_ba_dist.2)

plot(model_ba_bb.2)

car::Anova(model_scfa_dis.2, test.statistic ="F", type ="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Distance_traveled_norm
                     F Df  Df.res   Pr(>F)   
Total_SCFA_norm 8.9427  1 170.926 0.003197 **
Sex             5.3429  1  10.011 0.043373 * 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

car::Anova(model_scfa_bb.2, test.statistic ="F", type ="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Beam_breaks_norm
                     F Df  Df.res  Pr(>F)  
Total_SCFA_norm 5.2656  1 170.924 0.02297 *
Sex             4.8772  1  10.011 0.05167 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

car::Anova(model_ba_dist.2, test.statistic ="F", type ="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Distance_traveled_norm
                   F Df  Df.res  Pr(>F)  
Butyrate_norm 1.3875  1 178.519 0.24040  
Sex           5.6767  1   9.922 0.03863 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

car::Anova(model_ba_bb.2, test.statistic ="F", type ="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Beam_breaks_norm
                   F Df  Df.res  Pr(>F)  
Butyrate_norm 0.9599  1 178.547 0.32854  
Sex           5.2176  1   9.921 0.04566 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

set.seed(763)
r2_model_scfa_dis_mar<- partR2(model_scfa_dis.2, partvars = c("Total_SCFA_norm"), R2_type = "marginal", nboot = 10)

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r2_model_scfa_dis_mar$R2 %>% dplyr::filter(term == "Total_SCFA_norm") %>% dplyr::select(estimate) %>% pull()

[1] 0.02828478

r2_model_scfa_bb_mar <- partR2(model_scfa_bb.2, partvars = c("Total_SCFA_norm"), R2_type = "marginal", nboot = 10)

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r2_model_scfa_bb_mar$R2 %>% dplyr::filter(term == "Total_SCFA_norm") %>% dplyr::select(estimate) %>% pull()

[1] 0.01728646

r2_model_ba_dist_mar<- partR2(model_ba_dist.2, partvars = c("Butyrate_norm"), R2_type = "marginal", nboot = 10)

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r2_model_ba_dist_mar$R2 %>% dplyr::filter(term == "Butyrate_norm") %>% dplyr::select(estimate) %>% pull()

[1] 0.005993161

r2_model_ba_bb_mar<- partR2(model_ba_bb.2, partvars = c("Butyrate_norm"), R2_type = "marginal", nboot = 10)

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r2_model_ba_bb_mar$R2 %>% dplyr::filter(term == "Butyrate_norm") %>% dplyr::select(estimate) %>% pull()

[1] 0.004242537

#residuals
model_ba_scfa_dist_resid <- lmerTest::lmer(Distance_traveled_norm ~  Sex + (1 | Batch), data = SCFA_activity)
model_ba_scfa_bb_resid <- lmerTest::lmer(Beam_breaks_norm ~ Sex + (1 | Batch), data = SCFA_activity)

SCFA_activity <- SCFA_activity %>%
  dplyr::mutate(Distance_traveled_resid = residuals(model_ba_scfa_dist_resid)/sigma(model_ba_scfa_dist_resid), 
                Beam_breaks_resid = residuals(model_ba_scfa_bb_resid)/sigma(model_ba_scfa_bb_resid))

Fig4_tscfa_distance <- SCFA_activity %>%
  ggplot(aes(x= Total_SCFA_norm ,y=Distance_traveled_resid)) +
  geom_point(aes(shape = Sex, color = Category), alpha =0.7, size =0.6, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5, color = "black")+ 
  theme_classic(base_size = 10) +
  labs(y= "Adj. distance traveled", x = "Norm. total SCFAs")
Fig4_tscfa_distance

FigS5_butyrate_beambreaks<- SCFA_activity%>%
  ggplot(aes(x= Butyrate_norm ,y=Beam_breaks_resid)) +
  geom_point(aes(shape = Sex, color = Category), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5, color = "black")+ 
  theme_classic(base_size = 10) +
  labs(y= "Adj. beam breaks", x = "Norm. butyrate")
FigS5_butyrate_beambreaks

FigS5_tscfa_beambreaks <- SCFA_activity%>%
  ggplot(aes(x= Total_SCFA_norm ,y=Beam_breaks_resid)) +
  geom_point(aes(shape = Sex, color = Category), alpha =0.7, size =0.6, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5, color = "black")+ 
  theme_classic(base_size = 10) +
  labs(y= "Adj. beam breaks", x = "Norm. total SCFA")
FigS5_tscfa_beambreaks

Fig4_butyrate_distance <- SCFA_activity %>%
  ggplot(aes(x= Butyrate_norm ,y=Distance_traveled_resid)) +
  geom_point(aes(shape = Sex, color = Category), alpha =0.7, size =0.6, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_Category, label = c(expression(italic("Killed-CM")), expression(italic("Live-CM"))), name = "Treatment") +
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5, color = "black")+ 
  theme_classic(base_size = 10) +
  labs(y= "Adj. distance traveled", x = "Norm. butyrate") 
Fig4_butyrate_distance

targeted serum metabolites (LC_MS)

LCMS_serum.2 <- LCMS_serum %>%
  dplyr::filter(Sampletype == "Sample") %>%
  dplyr::mutate(GluA_Glu_ratio = Glutamic_acid/Glutamine) %>%
  tidyr::separate(sampleid, sep ="_", into = c("Metabolomics_Run", NA, NA), remove = FALSE) %>%
  dplyr::left_join(metadata, by ="MouseID")%>%
  dplyr::select(-Cortisol) # signal too weak

LCMS_serum.2 %>%
  tidyr::gather(molecule, conc_area, Corticosterone:GABA) %>%
  dplyr::mutate(Sex_Category = paste(Sex, Category, sep = "_")) %>%
  ggplot(aes(x= Sex_Category ,y= conc_area, fill=factor(Category))) +
  geom_boxplot(alpha =0.7, outlier.shape = NA) + 
  scale_fill_manual(values=color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  labs(fill = "Category") + 
  geom_point(alpha =0.7, size =0.5, position=position_jitterdodge(jitter.width=0.7),
             show.legend = T) +
  theme_classic(base_size = 11) + 
  theme(axis.title.x=element_blank()) +
  theme(legend.position="bottom") +
  scale_y_continuous(labels = function(x) format(x, scientific = TRUE)) +
  facet_wrap(molecule~., scales = "free", ncol = 4) +
  scale_x_discrete(labels=c("F\nKilled-\nCM", "F\nLive-\nCM", "M\nKilled-\nCM", "M\nLive-\nCM")) 

LCMS_serum_F <- LCMS_serum.2 %>%
  dplyr::filter(Sex == "F")
LCMS_serum_M <- LCMS_serum.2 %>%
  dplyr::filter(Sex == "M")

LCMS_serum_F_metabolites <- LCMS_serum_F %>%
  dplyr::select(sampleid, Corticosterone:GABA) %>%
  tibble::column_to_rownames(var ="sampleid")

LCMS_serum_M_metabolites <- LCMS_serum_M %>%
  dplyr::select(sampleid, Corticosterone:GABA) %>%
  tibble::column_to_rownames(var ="sampleid")

LCMS_serum_F_metadata <- LCMS_serum_F %>%
  dplyr::select(sampleid, MouseID, Metabolomics_Run, Batch, Category) %>%
  tibble::column_to_rownames(var ="sampleid")

LCMS_serum_M_metadata <- LCMS_serum_M %>%
  dplyr::select(sampleid, MouseID, Metabolomics_Run, Batch, Category) %>%
  tibble::column_to_rownames(var ="sampleid")

options(backup_options)

Maaslin_LCMS_F = Maaslin2(
    input_data = LCMS_serum_F_metabolites, 
    input_metadata = LCMS_serum_F_metadata, 
    output = "./data/Interim_output/Maaslin_LCMS_F", 
    fixed_effects = c("Category", "Metabolomics_Run"), 
    random_effects = c("Batch"), 
    min_prevalence = 0.7, 
    max_significance = 0.15)
Maaslin_LCMS_M = Maaslin2(
    input_data = LCMS_serum_M_metabolites, 
    input_metadata = LCMS_serum_M_metadata, 
    output = "./data/Interim_output/Maaslin_LCMS_M", 
    fixed_effects = c("Category", "Metabolomics_Run"), 
    random_effects = c("Batch"), 
    min_prevalence = 0.7, 
    max_significance = 0.15)

Maaslin_LCMS_F_results <- fread("./data/Interim_output/Maaslin_LCMS_F/all_results.tsv")
Maaslin_LCMS_M_results <- fread("./data/Interim_output/Maaslin_LCMS_M/all_results.tsv")

Maaslin_LCMS_F_results.2 <- Maaslin_LCMS_F_results %>%
  dplyr::filter(metadata == "Category") %>%
  dplyr::mutate(qval2 = p.adjust(pval, method = "BH")) %>%
  dplyr::arrange(qval2) 
Maaslin_LCMS_F_results.2

Maaslin_LCMS_M_results.2 <-Maaslin_LCMS_M_results %>%
  dplyr::filter(metadata == "Category") %>%
  dplyr::mutate(qval2 = p.adjust(pval, method = "BH")) %>%
  dplyr::arrange(qval2) 
Maaslin_LCMS_M_results.2

Fig4_Corticosterone <- LCMS_serum.2 %>% 
  dplyr::mutate(Corticosterone_10x4 = Corticosterone/10000) %>%
  ggplot(aes(x = Category, y = Corticosterone_10x4, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "Killed-\nCM", "Live" = "Live-\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y ="Corticosterone\n(PA) x e4") +
  theme(axis.text.x = element_text(face = "italic")) +
  facet_grid(.~Sex , scales ="free") +
  theme(strip.background = element_blank(),strip.text = element_blank()) 
Fig4_Corticosterone

FigS5_LCMS <- LCMS_serum.2 %>% 
  dplyr::mutate(Sex_Category =paste (Sex, Category, sep ="_")) %>%
  dplyr::select(Category, Sex_Category, Batch, Sex, Glutamic_acid, Glutamine, Histamine, Indolelactic_acid, Kynurenine, Thyroxine, Serotonin, Tryptophan, Kynurenic_acid, GABA) %>%
  dplyr::rename(Glu = Glutamic_acid, Gln = Glutamine, ILA = Indolelactic_acid, KYN = Kynurenine, T4 = Thyroxine, KYNA = Kynurenic_acid) %>% 
  tidyr::gather(metabolite, measurement, c(Glu:GABA)) %>%
  dplyr::mutate(measurement = as.numeric(measurement)) %>%
  dplyr::mutate(measurement_10x4 = measurement/10000) %>%
  ggplot(aes(x = Category, y = measurement_10x4, shape = Sex, color = Category)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_Category, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Killed" = "K", "Live" = "L")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = expression(paste("(PA/ml) x ", e^4))) +
  theme(axis.title.x=element_blank()) +
  ggh4x::facet_nested_wrap(~ metabolite + Sex, scales ="free_y", ncol =10, labeller=function(x) {x[1]}) +
  theme(legend.position="bottom") +
  theme(axis.text.x = element_text(face = "italic")) +
  theme(strip.background = element_blank())
FigS5_LCMS

untargeted serum metabolites (LC_MS)

Maaslin_ee <- Promethion_activity_ee.2_MF %>%
  dplyr::select(MouseID, EE_average_per_hour_residual) 

Maaslin_metadata <- LCMS_serum.2 %>% dplyr::select(sampleid, MouseID, Category, Sex, Batch, Metabolomics_Run)

xcms_peaklist <- LCMS_xcms_output %>%
  dplyr::select(mzmed, rtmed, R3_S11_6456:R3_S9_6438, R1_S13_5916:R1_S9_6189) %>% 
  dplyr::mutate(Sample = paste(mzmed, rtmed, sep ="/")) %>%
  dplyr::select(Sample, R3_S11_6456:R1_S9_6189, -R3_S37_6420) 


xcms_metadata <- xcms_peaklist %>% 
  tidyr::gather(var, value, -Sample) %>% 
  tidyr::spread(Sample, value) %>%
  dplyr::rename(sampleid = var) %>%
  dplyr::select(sampleid) %>%
  dplyr::left_join(Maaslin_metadata, by ="sampleid") %>%
  dplyr::left_join(Maaslin_ee, by ="MouseID") %>%
  dplyr::select(sampleid, Category, Sex, Batch, EE_average_per_hour_residual, Metabolomics_Run) 

xcms_metadata_F <- xcms_metadata %>% 
  dplyr::filter(Sex == "F")
xcms_metadata_M <- xcms_metadata %>% 
  dplyr::filter(Sex == "M")

females <- xcms_metadata_F %>% pull(sampleid)
males <- xcms_metadata_M %>% pull(sampleid)

xcms_peaklist_F <- xcms_peaklist %>% 
  dplyr::select(Sample, contains(females)) %>%
  dplyr::rename(sampleid = Sample)
xcms_peaklist_M <-xcms_peaklist %>% 
  dplyr::select(Sample, contains(males))%>%
  dplyr::rename(sampleid = Sample)

xcms_peaklist_F2 <- xcms_peaklist_F %>%
  dplyr::mutate(sampleid = sub('/', '_rt', sampleid)) %>%
  dplyr::mutate(sampleid = paste("mz", sampleid, sep = "")) %>%
  dplyr::rowwise() %>%
  dplyr::mutate(row_mean = mean(R1_S10_6303:R3_S9_6438)) %>% # create a row with mean intensity of peak
  dplyr::ungroup() %>% 
  dplyr::slice_max(row_mean, n = 5000) %>% # select top 5000 peaks according to mean intensity
  dplyr::select(-row_mean) %>%
  column_to_rownames(var = "sampleid") %>%
  dplyr::select(sort(names(.)))
xcms_metadata_F2 <- xcms_metadata_F %>%
  dplyr::arrange(sampleid) %>%
  column_to_rownames(var = "sampleid")

xcms_peaklist_M2 <- xcms_peaklist_M %>%
  dplyr::mutate(sampleid = sub('/', '_rt', sampleid)) %>%
  dplyr::mutate(sampleid = paste("mz", sampleid, sep = "")) %>%
  dplyr::rowwise() %>%
  dplyr::mutate(row_mean = mean(R1_S1_6342:R3_S8_6382)) %>% # create a row with mean intensity of peak
  dplyr::ungroup() %>% 
  dplyr::slice_max(row_mean, n = 5000) %>% # select top 5000 peaks according to mean intensity
  dplyr::select(-row_mean) %>%
  column_to_rownames(var = "sampleid") %>%
  dplyr::select(sort(names(.)))
xcms_metadata_M2 <- xcms_metadata_M %>%
  dplyr::arrange(sampleid) %>%
  column_to_rownames(var = "sampleid")

options(backup_options)

Xcms_Maaslin_F = Maaslin2(
    input_data = xcms_peaklist_F2, 
    input_metadata = xcms_metadata_F2, 
    output = "./data/Interim_output/Xcms_Maaslin_F", 
    fixed_effects = c("Category", "Metabolomics_Run"), 
    random_effects = c("Batch"), 
    min_prevalence = 0.7, 
    max_significance = 0.1, 
    plot_heatmap = FALSE, 
    plot_scatter = FALSE)

Xcms_Maaslin_M = Maaslin2(
    input_data = xcms_peaklist_M2, 
    input_metadata = xcms_metadata_M2, 
    output = "./data/Interim_output/Xcms_Maaslin_M", 
    fixed_effects = c("Category", "Metabolomics_Run"), 
    random_effects = c("Batch"), 
    min_prevalence = 0.7, 
    max_significance = 0.1, 
    plot_heatmap = FALSE, 
    plot_scatter = FALSE)

Xcms_Maaslin_F_EE = Maaslin2(
    input_data = xcms_peaklist_F2, 
    input_metadata = xcms_metadata_F2, 
    output = "./data/Interim_output/Xcms_Maaslin_F_EE", 
    fixed_effects = c("EE_average_per_hour_residual", "Metabolomics_Run"), 
    random_effects = c("Batch"), 
    min_prevalence = 0.7, 
    max_significance = 0.1, 
    plot_heatmap = FALSE, 
    plot_scatter = FALSE)

Xcms_Maaslin_M_EE = Maaslin2(
    input_data = xcms_peaklist_M2, 
    input_metadata = xcms_metadata_M2, 
    output = "./data/Interim_output/Xcms_Maaslin_M_EE", 
    fixed_effects = c("EE_average_per_hour_residual", "Metabolomics_Run"), 
    random_effects = c("Batch"), 
    min_prevalence = 0.7, 
    max_significance = 0.1, 
    plot_heatmap = FALSE, 
    plot_scatter = FALSE)

Xcms_Maaslin_F_results<- fread("./data/Interim_output/Xcms_Maaslin_F/all_results.tsv")
Xcms_Maaslin_M_results<- fread("./data/Interim_output/Xcms_Maaslin_M/all_results.tsv")

Xcms_Maaslin_F_EE_results<- fread("./data/Interim_output/Xcms_Maaslin_F_EE/all_results.tsv")
Xcms_Maaslin_M_EE_results<- fread("./data/Interim_output/Xcms_Maaslin_M_EE/all_results.tsv")

#Treatment
Xcms_Maaslin_F_stats <-Xcms_Maaslin_F_results %>% 
  as.data.frame() %>%
  dplyr::filter(metadata == "Category") %>%
  dplyr::mutate(t.score = coef/stderr) %>% 
  tidyr::separate(feature, sep = "_rt", into = c("m.z", "r.t")) %>%
  dplyr::mutate(m.z = sub("mz", "", m.z)) %>%
  dplyr::mutate(qval2 = p.adjust(pval, method = "BH")) %>%
  dplyr::rename(p.value = qval2) %>%
  dplyr::arrange(p.value) %>%
  dplyr::select(m.z, r.t, p.value, t.score)

Xcms_Maaslin_M_stats <-Xcms_Maaslin_M_results %>% 
  as.data.frame() %>%
  dplyr::filter(metadata == "Category") %>%
  dplyr::mutate(t.score = coef/stderr) %>% 
  tidyr::separate(feature, sep = "_rt", into = c("m.z", "r.t")) %>%
  dplyr::mutate(m.z = sub("mz", "", m.z)) %>%
  dplyr::mutate(qval2 = p.adjust(pval, method = "BH")) %>%
  dplyr::rename(p.value = qval2) %>%
  dplyr::arrange(p.value) %>%
  dplyr::select(m.z, r.t, p.value, t.score)

#EE
Xcms_Maaslin_F_EE_stats <-Xcms_Maaslin_F_EE_results %>% 
  as.data.frame() %>%
  dplyr::filter(metadata == "EE_average_per_hour_residual") %>%
  dplyr::mutate(t.score = coef/stderr) %>% 
  tidyr::separate(feature, sep = "_rt", into = c("m.z", "r.t")) %>%
  dplyr::mutate(m.z = sub("mz", "", m.z)) %>%
  dplyr::mutate(qval2 = p.adjust(pval, method = "BH")) %>%
  dplyr::rename(p.value = qval2) %>%
  dplyr::arrange(p.value) %>%
  dplyr::select(m.z, r.t, p.value, t.score)

Xcms_Maaslin_M_EE_stats <-Xcms_Maaslin_M_EE_results %>% 
  as.data.frame() %>%
  dplyr::filter(metadata == "EE_average_per_hour_residual") %>%
  dplyr::mutate(t.score = coef/stderr) %>% 
  tidyr::separate(feature, sep = "_rt", into = c("m.z", "r.t")) %>%
  dplyr::mutate(m.z = sub("mz", "", m.z)) %>%
  dplyr::mutate(qval2 = p.adjust(pval, method = "BH")) %>%
  dplyr::rename(p.value = qval2) %>%
  dplyr::arrange(p.value) %>%
  dplyr::select(m.z, r.t, p.value, t.score)

write.table(Xcms_Maaslin_F_stats, file = "./data/Interim_output/Xcms_Maaslin_F_stats.txt", sep = "," ,row.names = FALSE, quote = FALSE)
write.table(Xcms_Maaslin_M_stats, file = "./data/Interim_output/Xcms_Maaslin_M_stats.txt", sep = "," ,row.names = FALSE, quote = FALSE)

write.table(Xcms_Maaslin_F_EE_stats, file = "./data/Interim_output/Xcms_Maaslin_F_EE_stats.txt", sep = "," ,row.names = FALSE, quote = FALSE)
write.table(Xcms_Maaslin_M_EE_stats, file = "./data/Interim_output/Xcms_Maaslin_M_EE_stats.txt", sep = "," ,row.names = FALSE, quote = FALSE)

MetaboAnalyst Output

MA_int_F_EE <- fread("./data/Metabolomics/metaboanalyst/MetaboAnalyst_F_EE/mummichog_integ_pathway_enrichment.csv")
MA_gsea_F_EE <- fread("./data/Metabolomics/metaboanalyst/MetaboAnalyst_F_EE/mummichog_fgsea_pathway_enrichment.csv")

#Females
MA_gsea_F_CM.2 <- MA_gsea_F_EE %>%
  dplyr::rename(Pathway = V1)%>%
  dplyr::select(Pathway, NES) 

MA_int_F_EE.2 <- MA_int_F_EE %>% 
  dplyr::rename(Pathway = V1)%>%
  dplyr::select(Pathway, Combined_Pvals) %>%
  dplyr::filter(Combined_Pvals <= 0.05) %>%
  dplyr::left_join(MA_gsea_F_CM.2, by = "Pathway") %>%
  dplyr::arrange(-NES) %>%
  dplyr::mutate(analysis = "F")%>%
  dplyr::mutate(variable = "EE")

Fig4_F_EE_pathway <- MA_int_F_EE.2 %>% 
  dplyr::arrange(NES) %>% 
  dplyr::mutate(Pathway = factor(Pathway, levels = Pathway)) %>% 
  ggplot(aes(y = NES, x = Pathway, fill = Combined_Pvals)) +
  geom_bar(stat = "identity") +
  theme_classic(base_size = 10) +
  scale_fill_gradient(high = "#fed799", low = "#b73779", na.value = NA, name = "Combined\np-value") + 
  coord_flip() +
  ggtitle("F - EE")+ 
  theme(plot.title = element_text(hjust = 0.5))+
  theme(axis.title.y=element_blank()) +
  theme(legend.position = 'right') +
   theme(
        legend.key.height = unit(0.5, 'cm'), 
        legend.key.width = unit(0.5, 'cm')) 
Fig4_F_EE_pathway 

Fig. 4 - Changes of metabolism in relation to activity and energy expenditure.

Fig4_align <- cowplot::align_plots(Fig4_totalSCFA + theme(legend.position="none"),
                                      Fig4_tscfa_distance + theme(legend.position="none"),
                                      Fig4_butyrate+ theme(legend.position="none"),
                                      Fig4_butyrate_distance + theme(legend.position="none"),
                                      Fig4_Corticosterone+ theme(legend.position="none"),
                                      align = 'v', axis = 'l')

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

Fig4_1 <- cowplot::plot_grid(Fig4_align[[1]],
                                  Fig4_align[[3]],
                                  Fig4_align[[2]],
                                Fig4_align[[4]],
                                  ncol=4, nrow=1, labels = c("A", "B", "C", "D"),label_size = 12, rel_widths = c(2, 2,1.8, 1.8))

Fig4_2 <- cowplot::plot_grid(Fig4_align[[5]],
                                  Fig4_F_EE_pathway,
                                Legend_treatment_sex, 
                                  ncol=3, nrow=1, labels = c( "E", "F", NA),label_size = 12, rel_widths = c(2, 4.2, 1.4))


Figure4 <- cowplot::plot_grid(NA, 
                                 Fig4_1, 
                             Fig4_2,
                             ncol=1, nrow = 3, rel_heights = c(0.1, 1,1))

Warning: Cannot convert object of class logical into a grob.Warning: Removed 1 rows containing missing values (geom_text).

Figure4

ggsave("./Figure4.pdf", plot = Figure4, device = cairo_pdf,  
  width = 17,
  height = 10.5,
  units = "cm")

Fig. S5 - Candidate metabolites assessed in cecal contents and serum samples.

FigS5_align <- cowplot::align_plots(FigS5_SCFAs + theme(legend.position="none"),
                                         FigS5_tscfa_beambreaks + theme(legend.position="none"),
                                         FigS5_butyrate_beambreaks+ theme(legend.position="none"),
                                         FigS5_LCMS + theme(legend.position="none"),
                                         align = 'v', axis = 'l')

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

FigS5_1 <- cowplot::plot_grid(FigS5_align[[2]], 
                                   FigS5_align[[3]], 
                                   ncol = 2, nrow=1, labels = c("B", "C"), label_size = 12, rel_widths = c(1,1))

FigS5_2 <- cowplot::plot_grid(FigS5_1,
                                Fig3_legend_treatment_sex,
                                   ncol = 1, nrow=2, rel_heights = c(1,0.5))

FigS5_3<- cowplot::plot_grid(FigS5_align[[1]], 
                                FigS5_2,
                                   ncol = 2, nrow=1, labels = c("A", NA), label_size = 12, rel_widths = c(1, 1))

FigureS5 <- cowplot::plot_grid(NA,
                                  FigS5_3, 
                                  NA,
                                  FigS5_align[[4]],
                                   ncol = 1, nrow=5, labels = c(NA, "A", NA, "D"),label_size = 12, rel_heights = c(0.1,1, 0.1, 1))

Warning: Cannot convert object of class logical into a grob.Warning: Removed 1 rows containing missing values (geom_text).Warning: Cannot convert object of class logical into a grob.

FigureS5

ggsave("./FigureS5.pdf", plot = Fig_supp.met, device = cairo_pdf,  
  width = 17.3,
  height = 18,
  units = "cm")

Regrouping mice by final C. minuta content corroborates treatment effects

Figure S6

median_Cmin_per_gCC <- median(metadata_Cmin$qPCR_Cmin_SQ_per_gCC) 

metadata_Cmin_HL <- metadata_Cmin %>% 
  dplyr::mutate(Cmin_load = ifelse(qPCR_Cmin_SQ_per_gCC > median_Cmin_per_gCC, "High", "Low")) %>%
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels = c("Low", "High"))) 

FigS6_high_Low <- metadata_Cmin_HL %>%
  dplyr::mutate(Cmin_per_gCC_1e7 = qPCR_Cmin_SQ_per_gCC / 1e7) %>% 
  ggplot(aes(x = Category, y = Cmin_per_gCC_1e7, shape = Sex, color = Cmin_load)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Category), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values=color_HL, label = c("Low-CM", "High-CM"),name = "Group") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = expression(paste("C. minuta (GE/g) x ", e^7))) +
  theme(axis.text.x = element_text(face = "italic")) +
  geom_hline(yintercept=(median_Cmin_per_gCC/ 1e7), linetype="dashed", color = "black") +
  scale_x_discrete(labels=c("Killed" = "Killed\nCM", "Live" = "Live\nCM")) 
FigS6_high_Low

set.seed(763)
Promethion_Food_HL <- metadata_Cmin_HL %>%
  dplyr::select(MouseID, Cmin_load)  %>%
  dplyr::left_join(Promethion_Food) %>% 
  tidyr::drop_na(Cmin_load, Promethion_weight_norm)

Joining, by = "MouseID"

Promethion_Food_HL$FI_kcal_day_norm <- predict(bestNormalize(Promethion_Food_HL$FI_kcal_day))

options(contrasts=c("contr.sum", "contr.poly"))
model_food_HL <- lmerTest::lmer(FI_kcal_day_norm ~ Cmin_load*Sex + Sex*Promethion_weight_norm +Promethion_Cabinet + (Cmin_load|Batch/Sex/Promethion_Cabinet), data = Promethion_Food_HL)

boundary (singular) fit: see help('isSingular')

lmerTest::step(model_food_HL, keep = "Cmin_load")

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -1.1e-02boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with max|grad| = 0.00579757 (tol = 0.002, component 1)boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                                                          Eliminated npar  logLik    AIC     LRT Df Pr(>Chisq)
<none>                                                                 17 -218.25 470.50                      
Cmin_load in (Cmin_load | Sex:Batch)                               1   15 -218.25 466.50 0.00000  2     1.0000
(1 | Sex:Batch)                                                    2   14 -218.25 464.50 0.00001  1     0.9971
Cmin_load in (Cmin_load | Promethion_Cabinet:(Sex:Batch))          3   12 -218.38 460.76 0.25714  2     0.8794
(1 | Promethion_Cabinet:(Sex:Batch))                               4   11 -218.38 458.76 0.00000  1     1.0000
Cmin_load in (Cmin_load | Batch)                                   5    9 -218.59 455.19 0.42441  2     0.8088
(1 | Batch)                                                        6    8 -218.63 453.26 0.07053  1     0.7906

Backward reduced fixed-effect table:
                           Eliminated Df Sum of Sq    RSS     AIC F value   Pr(>F)    
Cmin_load:Sex                       1  1    0.1436 117.57 -46.608  0.1957 0.658795    
Sex:Promethion_weight_norm          2  1    0.1146 117.69 -48.445  0.1569 0.692559    
Promethion_Cabinet                  3  1    1.3217 119.01 -48.580  1.8193 0.179277    
Cmin_load                           0  1    0.1039 119.11 -50.434  0.1423 0.706510    
Sex                                 0  1    8.7229 127.73 -38.767 11.9473 0.000698 ***
Promethion_weight_norm              0  1    3.7749 122.78 -45.365  5.1703 0.024281 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
FI_kcal_day_norm ~ Cmin_load + Sex + Promethion_weight_norm

no effect of Cmin_load

options(contrasts=c("contr.sum", "contr.poly"))
model_food_HL.2 <- lm(FI_kcal_day_norm ~ Cmin_load+Sex + Promethion_weight_norm, data = Promethion_Food_HL)
shapiro.test(residuals(model_food_HL.2))

    Shapiro-Wilk normality test

data:  residuals(model_food_HL.2)
W = 0.98146, p-value = 0.02484

anova(lm(residuals(model_food_HL.2)~fitted.values(model_food_HL.2)))

Analysis of Variance Table

Response: residuals(model_food_HL.2)
                                Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_food_HL.2)   1   0.00 0.00000       0      1
Residuals                      165 119.01 0.72126               

car::Anova(model_food_HL.2, test.statistic ="F", type ="II")

Anova Table (Type II tests)

Response: FI_kcal_day_norm
                        Sum Sq  Df F value   Pr(>F)    
Cmin_load                0.104   1  0.1423 0.706510    
Sex                      8.723   1 11.9473 0.000698 ***
Promethion_weight_norm   3.775   1  5.1703 0.024281 *  
Residuals              119.008 163                     
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model_food_HL.eff <- lm(FI_kcal_day ~ Cmin_load+Sex + Promethion_weight_norm, data = Promethion_Food_HL)
effects::effect("Cmin_load", model_food_HL.eff, se = TRUE) %>% as.data.frame

model_food_HL_resid <- lm(FI_kcal_day_norm ~ Sex + Promethion_weight_norm, data = Promethion_Food_HL)

Promethion_Food_HL$FI_kcal_day_resid_HL <- residuals(model_food_HL_resid)/sigma(model_food_HL_resid)

FigS6_food <- Promethion_Food_HL %>% 
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels= c("Low", "High"))) %>% 
  ggplot(aes(x = Cmin_load, y = FI_kcal_day_resid_HL, shape = Sex, color = Cmin_load)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Cmin_load), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_HL) +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Low" = "Low\nCM", "High" = "High\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Adj. food intake") +
  theme(axis.text.x = element_text(face = "italic")) 
FigS6_food

options(contrasts=c("contr.sum", "contr.poly"))
set.seed(650)
Promethion_Food_HL <- Promethion_Food_HL %>%
  dplyr::mutate(Feed_eficiency_norm = predict(bestNormalize(Feed_eficiency)))

model_feeding_eff_HL <- lmer(Feed_eficiency_norm ~ Sex*Cmin_load + Promethion_Cabinet + (1|Batch), data = Promethion_Food_HL)
step(model_feeding_eff_HL)

Backward reduced random-effect table:

            Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)    
<none>                    7 -225.24 464.48                         
(1 | Batch)          0    6 -237.92 487.83 25.351  1  4.778e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                   Eliminated  Sum Sq Mean Sq NumDF   DenDF F value  Pr(>F)  
Promethion_Cabinet          1 0.02893 0.02893     1 151.545  0.0402 0.84130  
Sex:Cmin_load               2 0.68520 0.68520     1 162.932  0.9586 0.32898  
Sex                         3 0.23681 0.23681     1   9.538  0.3338 0.57679  
Cmin_load                   0 2.99525 2.99525     1 163.922  4.2210 0.04151 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Feed_eficiency_norm ~ Cmin_load + (1 | Batch)

model_feeding_eff_HL.2 <- lmer(Feed_eficiency_norm ~ Cmin_load + (1|Batch), data = Promethion_Food_HL)
shapiro.test(residuals(model_feeding_eff_HL.2))

    Shapiro-Wilk normality test

data:  residuals(model_feeding_eff_HL.2)
W = 0.99084, p-value = 0.3608

anova(lm(residuals(model_feeding_eff_HL.2)~fitted.values(model_feeding_eff_HL.2)))

Analysis of Variance Table

Response: residuals(model_feeding_eff_HL.2)
                                       Df  Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_feeding_eff_HL.2)   1   1.107 1.10725  1.6738 0.1976
Residuals                             165 109.152 0.66153               

plot(model_feeding_eff_HL.2)

Anova(model_feeding_eff_HL.2, type ="II", test.statistic ="F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Feed_eficiency_norm
               F Df Df.res  Pr(>F)  
Cmin_load 4.1482  1 163.97 0.04329 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model_feeding_eff_HL.eff <- lmer(Feed_eficiency ~ Cmin_load + (1|Batch), data = Promethion_Food_HL)
effects::effect("Cmin_load", model_feeding_eff_HL.eff, se = TRUE) %>% as.data.frame

model_feeding_eff_HL.resid <- lmer(Feed_eficiency_norm ~ (1|Batch), data = Promethion_Food_HL)

Promethion_Food_HL$Feed_efficency_resid_HL <- residuals(model_feeding_eff_HL.resid)/sigma(model_feeding_eff_HL.resid)

FigS6_feed_eff <- Promethion_Food_HL %>%
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels= c("Low", "High"))) %>% 
  ggplot(aes(x = Cmin_load, y = Feed_efficency_resid_HL, shape = Sex, color = Cmin_load)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Cmin_load), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_HL) +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Low" = "Low\nCM", "High" = "High\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Adj. feed efficiency") +
  theme(axis.text.x = element_text(face = "italic")) 
FigS6_feed_eff

set.seed(628)
metadata_bc_HL <- metadata_Cmin_HL %>%
  dplyr::select(MouseID, Cmin_load)  %>%
  dplyr::left_join(metadata_bc, by ="MouseID") %>% 
  tidyr::drop_na(Cmin_load, bombcal_Calorific_Value_kcal_total_day)
 

metadata_bc_HL <- metadata_bc_HL %>%
  dplyr::mutate(bombcal_Calorific_Value_kcal_total_day_norm = predict(bestNormalize(bombcal_Calorific_Value_kcal_total_day)),
                Promethion_weight_norm <- predict(bestNormalize(Promethion_weight)), 
                bombcal_Dry_sample_mass_norm <- predict(bestNormalize(bombcal_Dry_sample_mass)))

model_Bomb_cal_HL <- lmerTest::lmer(bombcal_Calorific_Value_kcal_total_day_norm ~ Cmin_load*Sex + Promethion_weight_norm +(Cmin_load|Batch/Sex/Promethion_Cabinet), data = metadata_bc_HL)

boundary (singular) fit: see help('isSingular')

lmerTest::step(model_Bomb_cal_HL, keep = "Cmin_load")

Warning: Model failed to converge with max|grad| = 0.00204632 (tol = 0.002, component 1)boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 2 negative eigenvalues: -2.4e-02 -1.3e-01boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -2.6e-01boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
Warning: Model failed to converge with 1 negative eigenvalue: -4.9e-01Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -2.6e-01Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -2.6e-01

Backward reduced random-effect table:

                                                          Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)   
<none>                                                                 15 -110.47 250.94                        
Cmin_load in (Cmin_load | Sex:Batch)                               1   13 -110.47 246.94 0.0000  2   1.000000   
(1 | Sex:Batch)                                                    2   12 -110.47 244.94 0.0000  1   0.999903   
Cmin_load in (Cmin_load | Batch)                                   3   10 -110.56 241.12 0.1864  2   0.911007   
Cmin_load in (Cmin_load | Promethion_Cabinet:(Sex:Batch))          4    8 -110.63 237.25 0.1298  2   0.937172   
(1 | Batch)                                                        5    7 -110.63 235.25 0.0004  1   0.983430   
(1 | Promethion_Cabinet:(Sex:Batch))                               0    6 -115.33 242.66 9.4111  1   0.002157 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                       Eliminated Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
Cmin_load:Sex                   1 0.8972  0.8972     1 82.646  1.5933 0.2104077    
Sex                             2 0.2487  0.2487     1 19.272  0.4399 0.5150264    
Cmin_load                       0 2.0141  2.0141     1 83.901  3.5741 0.0621396 .  
Promethion_weight_norm          0 8.0426  8.0426     1 56.535 14.2717 0.0003825 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
bombcal_Calorific_Value_kcal_total_day_norm ~ Cmin_load + Promethion_weight_norm + (1 | Promethion_Cabinet:(Sex:Batch))

options(contrasts=c("contr.sum", "contr.poly"))

model_Bomb_cal_HL.2 <- lmer(bombcal_Calorific_Value_kcal_total_day_norm ~ Cmin_load + Promethion_weight_norm+ (1 | Promethion_Cabinet:(Sex:Batch)), data = metadata_bc_HL)
stats::shapiro.test(residuals(model_Bomb_cal_HL.2))

    Shapiro-Wilk normality test

data:  residuals(model_Bomb_cal_HL.2)
W = 0.99079, p-value = 0.7991

anova(lm(residuals(model_Bomb_cal_HL.2)~fitted.values(model_Bomb_cal_HL.2)))

Analysis of Variance Table

Response: residuals(model_Bomb_cal_HL.2)
                                   Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_Bomb_cal_HL.2)  1  0.529 0.52857  1.0551 0.3072
Residuals                          86 43.082 0.50096               

plot(model_Bomb_cal_HL.2)

options(contrasts=c("contr.sum", "contr.poly"))
car::Anova(model_Bomb_cal_HL.2, test.statistic="F", type = "II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: bombcal_Calorific_Value_kcal_total_day_norm
                             F Df Df.res    Pr(>F)    
Cmin_load               3.4532  1 83.871 0.0666359 .  
Promethion_weight_norm 12.9316  1 56.017 0.0006835 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model_Bomb_cal_HL_eff <- lmer(bombcal_Calorific_Value_kcal_total_day ~ Cmin_load + Promethion_weight_norm+ (1 | Promethion_Cabinet:(Sex:Batch)), data = metadata_bc_HL)

effect(c("Cmin_load"), model_Bomb_cal_HL_eff, se =TRUE) %>% as.data.frame

model_Bomb_cal_HL_resid <- lmer(bombcal_Calorific_Value_kcal_total_day_norm ~ Sex + Promethion_weight_norm+ (1 | Promethion_Cabinet:(Sex:Batch)), data = metadata_bc_HL)

metadata_bc_HL$bombcal_Calorific_Value_kcal_total_day_resid_HL <- residuals(model_Bomb_cal_HL_resid)/sigma(model_Bomb_cal_HL_resid)

FigS6_bomb_cal <- metadata_bc_HL %>%
  drop_na(Cmin_load) %>% 
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels= c("Low", "High"))) %>% 
  ggplot(aes(x = Cmin_load, y = bombcal_Calorific_Value_kcal_total_day_resid_HL, shape = Sex, color = Cmin_load)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Cmin_load), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_HL) +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Low" = "Low\nCM", "High" = "High\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Adj. fecal energy content") +
  theme(axis.text.x = element_text(face = "italic")) 
FigS6_bomb_cal

Beam Breaks

set.seed(259)

Promethion_activity_HL <- metadata_Cmin_HL %>%
  dplyr::select(MouseID, Cmin_load)  %>%
  dplyr::left_join(Promethion_activity, by ="MouseID") %>% 
  tidyr::drop_na(Cmin_load, Beam_breaks)

Promethion_activity_HL$Beam_breaks_norm <- predict(bestNormalize(Promethion_activity_HL$Beam_breaks))

options(contrasts=c("contr.sum", "contr.poly"))

model_bb_HL<-(lmer(Beam_breaks_norm ~ Cmin_load*Sex + Promethion_Cabinet + (Cmin_load|Batch), Promethion_activity_HL))
step(model_bb_HL, keep ="Cmin_load")

Backward reduced random-effect table:

                                 Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)    
<none>                                         9 -221.60 461.19                         
Cmin_load in (Cmin_load | Batch)          1    7 -222.16 458.31  1.122  2     0.5707    
(1 | Batch)                               0    6 -238.09 488.17 31.855  1  1.661e-08 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                   Eliminated Sum Sq Mean Sq NumDF   DenDF F value  Pr(>F)  
Promethion_Cabinet          1 0.0587  0.0587     1 164.730  0.1023 0.74948  
Cmin_load:Sex               2 0.1064  0.1064     1 175.991  0.1865 0.66640  
Cmin_load                   0 2.0815  2.0815     1 176.694  3.6581 0.05741 .
Sex                         0 3.4091  3.4091     1   9.588  5.9913 0.03535 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Beam_breaks_norm ~ Cmin_load + Sex + (1 | Batch)

options(contrasts=c("contr.sum", "contr.poly"))
model_bb_HL.2<-(lmer(Beam_breaks_norm ~ Cmin_load + (1|Batch), Promethion_activity_HL))
stats::shapiro.test(resid(model_bb_HL.2))

    Shapiro-Wilk normality test

data:  resid(model_bb_HL.2)
W = 0.9904, p-value = 0.2703

anova(lm(residuals(model_bb_HL.2)~fitted.values(model_bb_HL.2)))

Analysis of Variance Table

Response: residuals(model_bb_HL.2)
                              Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_bb_HL.2)   1  0.450 0.45046  0.8434 0.3597
Residuals                    178 95.074 0.53412               

plot(model_bb_HL.2)

car::Anova(model_bb_HL.2, test.statistic ="F", type ="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: Beam_breaks_norm
               F Df Df.res  Pr(>F)  
Cmin_load 3.0085  1  175.4 0.08458 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model_bb_HL.eff<-(lmer(Beam_breaks ~ Cmin_load + (1|Batch), Promethion_activity_HL))
effects::effect("Cmin_load", model_bb_HL.eff, se =TRUE) %>% as.data.frame()

model_bb_HL.resid<-(lmerTest::lmer(Beam_breaks_norm ~ (1 | Batch), Promethion_activity_HL))

Promethion_activity_HL$BeamBreaks_HL_resid <- residuals(model_bb_HL.resid)/sigma(model_bb_HL.resid)

FigS6_bb_HL <- Promethion_activity_HL %>%
  drop_na(Cmin_load) %>% 
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels= c("Low", "High"))) %>% 
  ggplot(aes(x = Cmin_load, y = Beam_breaks_norm, shape = Sex, color = Cmin_load)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Cmin_load), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_HL) +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Low" = "Low\nCM", "High" = "High\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Adj. beam breaks") +
  theme(axis.text.x = element_text(face = "italic")) 
FigS6_bb_HL

Distance traveled

set.seed(312) 
Promethion_activity_HL$Distance_traveled_norm<- predict(bestNormalize(Promethion_activity_HL$Distance_traveled))

model_distance_HL<-(lmer(Distance_traveled_norm ~ Cmin_load*Sex + Promethion_Cabinet + (Cmin_load|Batch), Promethion_activity_HL))

boundary (singular) fit: see help('isSingular')

lmerTest::step(model_distance_HL, keep = "Cmin_load")

Backward reduced random-effect table:

                                 Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)    
<none>                                         9 -222.01 462.02                         
Cmin_load in (Cmin_load | Batch)          1    7 -222.50 459.01  0.990  2     0.6096    
(1 | Batch)                               0    6 -241.49 494.97 37.964  1  7.205e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                   Eliminated Sum Sq Mean Sq NumDF   DenDF F value  Pr(>F)  
Promethion_Cabinet          1 0.0244  0.0244     1 165.109  0.0424 0.83709  
Cmin_load:Sex               2 0.2097  0.2097     1 175.957  0.3669 0.54549  
Cmin_load                   0 0.9283  0.9283     1 176.545  1.6284 0.20360  
Sex                         0 3.4964  3.4964     1   9.849  6.1336 0.03307 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
Distance_traveled_norm ~ Cmin_load + Sex + (1 | Batch)

model_distance_HL.2<-(lmer(Distance_traveled_norm ~ Cmin_load*Sex + (1|Batch), Promethion_activity_HL))
shapiro.test(residuals(model_distance_HL.2))

    Shapiro-Wilk normality test

data:  residuals(model_distance_HL.2)
W = 0.98954, p-value = 0.2095

anova(lm(residuals(model_distance_HL.2)~fitted.values(model_distance_HL.2)))

Analysis of Variance Table

Response: residuals(model_distance_HL.2)
                                    Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_distance_HL.2)   1  0.339 0.33916  0.6338  0.427
Residuals                          178 95.258 0.53516               

car::Anova(model_distance_HL.2, type ="III", test.statistic ="F")

Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)

Response: Distance_traveled_norm
                   F Df  Df.res  Pr(>F)  
(Intercept)   0.0094  1   9.812 0.92474  
Cmin_load     1.3316  1 175.957 0.25009  
Sex           6.0022  1   9.812 0.03469 *
Cmin_load:Sex 0.3593  1 175.957 0.54969  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

emmeans::joint_tests(model_distance_HL.2, by ="Sex")

Sex = F:
 model term df1    df2 F.ratio p.value
 Cmin_load    1 174.36   0.133  0.7159

Sex = M:
 model term df1    df2 F.ratio p.value
 Cmin_load    1 171.24   1.824  0.1787

model_distance_HL.eff<-(lmer(Distance_traveled ~ Cmin_load*Sex  + (1|Batch), Promethion_activity_HL))
effects::effect("Cmin_load*Sex", model_distance_HL.eff, se =TRUE) %>% as.data.frame

model_distance_HL.resid <-(lmer(Distance_traveled_norm ~ Sex + (1|Batch), Promethion_activity_HL))

Promethion_activity_HL$Distance_traveled_resid_HL <- residuals(model_distance_HL.resid)/sigma(model_distance_HL.resid)

FigS6_distance_HL <- Promethion_activity_HL %>%
  drop_na(Cmin_load) %>% 
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels= c("Low", "High"))) %>% 
  ggplot(aes(x = Cmin_load, y = Distance_traveled_resid_HL, shape = Sex, color = Cmin_load)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Cmin_load), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_HL) +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Low" = "Low\nCM", "High" = "High\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Adj. distance traveled") +
  theme(axis.text.x = element_text(face = "italic"))  + 
  facet_grid(.~Sex) 
FigS6_distance_HL

FigS6_HL_legend <- cowplot::get_legend(metadata_Cmin_HL %>% 
  dplyr::mutate(Sex = ifelse(Sex == "F", "Female", "Male"), Cmin_load = ifelse(Cmin_load == "Low", "Low-CM", "High-CM")) %>%
  dplyr::mutate(Sex_Cmin_load = paste(Sex, Cmin_load, sep = " ")) %>%
  ggplot() +
  geom_point(aes(x = Sex_Cmin_load, y = qPCR_Cmin_per_gCC, shape = Sex_Cmin_load, color = Sex_Cmin_load), size =3 , alpha =1,
             show.legend = T) +
  scale_shape_manual(values=c(16, 16,4, 4)) + 
  scale_color_manual(values= c(color_HL, color_HL)) +
  theme_classic(base_size = 10) +
  theme(legend.title = element_blank()))


FigS6_align <- cowplot::align_plots(FigS6_high_Low + theme(legend.position="none"),
                                            FigS6_food + theme(legend.position="none"),
                                            FigS6_feed_eff + theme(legend.position="none"),
                                            FigS6_bomb_cal + theme(legend.position="none"),
                                          FigS6_bb_HL + theme(legend.position="none"),
                                          FigS6_distance_HL + theme(legend.position="none"),
                                            align = 'v', axis = 'l')

FigS6_1 <- cowplot::plot_grid(FigS6_align[[1]],  
                               FigS6_align[[2]], 
                               FigS6_align[[3]], 
                               FigS6_align[[4]], 
                               ncol=4, nrow=1, labels = c("A", "B", "C", "D"), label_size = 12, rel_widths = c(1,1,1,1))
FigS6_2 <- cowplot::plot_grid(NA,  
                               FigS6_align[[5]], 
                               NA, 
                               FigS6_align[[6]], 
                               FigS6_HL_legend,
                               ncol=5, nrow=1, labels = c(NA, "E", NA, "F", NA), label_size = 12, rel_widths = c(0.3,1,0.3,1.8,0.8))

Warning: Cannot convert object of class logical into a grob.Warning: Cannot convert object of class logical into a grob.

FigureS6 <- cowplot::plot_grid(FigS6_1,  
                               FigS6_2, 
                               ncol=1, nrow=2, rel_heights = c(1,1))

Warning: Removed 1 rows containing missing values (geom_text).Warning: Removed 1 rows containing missing values (geom_text).Warning: Removed 1 rows containing missing values (geom_text).

FigureS6

ggsave("./FigureS6.pdf", plot = FigureS6, device = cairo_pdf,  
  width = 15.5,
  height = 10.5,
  units = "cm")

FigureS7

set.seed(759)

metadata_Cmin_HL_qPCR <- metadata_Cmin_HL %>%
  tidyr::drop_na(Cmin_load, qPCR_16S_SQ_per_gCC) %>%
  dplyr::mutate(qPCR_16S_SQ_per_gCC_norm = predict(bestNormalize(qPCR_16S_SQ_per_gCC)))

model_qPCR16S_HL<- lmerTest::lmer(qPCR_16S_SQ_per_gCC_norm ~ Cmin_load*Sex + T6_weight + Plate + (1|qPCR_Round/Batch/Sex), data = metadata_Cmin_HL_qPCR)

Warning: unable to evaluate scaled gradientWarning:  Hessian is numerically singular: parameters are not uniquely determined

lmerTest::step(model_qPCR16S_HL, keep = "Cmin_load")

Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -1.2e-08Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -1.2e-08Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -1.2e-08

Backward reduced random-effect table:

                             Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)  
<none>                                    11 -250.04 522.07                       
(1 | Sex:(Batch:qPCR_Round))          1   10 -250.04 520.07 0.0000  1     1.0000  
(1 | qPCR_Round)                      2    9 -250.04 518.07 0.0000  1     1.0000  
(1 | Batch:qPCR_Round)                0    8 -251.80 519.60 3.5235  1     0.0605 .
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

              Eliminated  Sum Sq Mean Sq NumDF   DenDF F value    Pr(>F)    
T6_weight              1  0.2349  0.2349     1 170.596  0.2863 0.5932750    
Cmin_load:Sex          2  0.4815  0.4815     1 147.792  0.5894 0.4438806    
Sex                    3  1.0246  1.0246     1   9.734  1.2591 0.2887426    
Plate                  4  4.7372  2.3686     2  23.095  2.9116 0.0745347 .  
Cmin_load              0 10.6378 10.6378     1 168.331 12.9777 0.0004147 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
qPCR_16S_SQ_per_gCC_norm ~ Cmin_load + (1 | Batch:qPCR_Round)

model_qPCR16S_HL.2<- lmer(qPCR_16S_SQ_per_gCC_norm ~ Cmin_load + Plate + (1 | Batch:qPCR_Round), data = metadata_Cmin_HL_qPCR)

shapiro.test(residuals(model_qPCR16S_HL.2))

    Shapiro-Wilk normality test

data:  residuals(model_qPCR16S_HL.2)
W = 0.9793, p-value = 0.008588

anova(lm(residuals(model_qPCR16S_HL.2) ~ fitted(model_qPCR16S_HL.2))) 

Analysis of Variance Table

Response: residuals(model_qPCR16S_HL.2)
                            Df  Sum Sq Mean Sq F value Pr(>F)
fitted(model_qPCR16S_HL.2)   1   0.689 0.68946  0.8901 0.3467
Residuals                  179 138.652 0.77459               

plot(model_qPCR16S_HL.2)

car::Anova(model_qPCR16S_HL.2, type = "II", test.statistic ="F")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: qPCR_16S_SQ_per_gCC_norm
               F Df Df.res   Pr(>F)   
Cmin_load 8.4553  1 174.88 0.004112 **
Plate     2.8213  2  27.54 0.076799 . 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

model_qPCR16S_HL.eff<- lmer(qPCR_16S_SQ_per_gCC~ Cmin_load + Plate + (1 | Batch:qPCR_Round), data = metadata_Cmin_HL_qPCR)

Warning: Model may not have converged with 1 eigenvalue close to zero: 1.8e-17

effects::effect("Cmin_load", model_qPCR16S_HL.eff, se =TRUE) %>% as.data.frame

FigS7_mbiomass_HL <- metadata_Cmin_HL_qPCR %>%
  drop_na(Cmin_load) %>% 
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels= c("Low", "High"))) %>% 
  dplyr::mutate(qPCR_16S_SQ_per_gCC_e10 = qPCR_16S_SQ_per_gCC/1e10) %>% 
  ggplot(aes(x = Cmin_load, y = qPCR_16S_SQ_per_gCC_e10, shape = Sex, color = Cmin_load)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Cmin_load), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_HL, label = c("Low-CM", "High-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Low" = "Low\nCM", "High" = "High\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = expression(paste("Microbes (GE/g) x ", e^10))) +
  theme(axis.text.x = element_text(face = "italic")) 
FigS7_mbiomass_HL

bombcal ~ microbial biomass/cmin

options(contrasts=c("contr.sum", "contr.poly"))
set.seed(259)
metadata_Cmin_HL_qPCR_bc <- metadata_Cmin_HL_qPCR %>%
  dplyr::select(MouseID, qPCR_16S_SQ_per_gCC, qPCR_Cmin_SQ_per_gCC, qPCR_Round) %>%
  dplyr::left_join(metadata_bc_HL, by ="MouseID") %>%
  tidyr::drop_na(bombcal_Calorific_Value_kcal_total_day) %>%
  dplyr::mutate(qPCR_16S_SQ_per_gCC_norm = predict(bestNormalize(qPCR_16S_SQ_per_gCC)), 
                qPCR_Cmin_SQ_per_gCC_norm = predict(bestNormalize(qPCR_Cmin_SQ_per_gCC)), 
                bombcal_Calorific_Value_kcal_total_day_norm = predict(bestNormalize(bombcal_Calorific_Value_kcal_total_day)), 
                Promethion_weight_norm = predict(bestNormalize(Promethion_weight)))

model_bombcal_Cmin <- lmerTest::lmer(bombcal_Calorific_Value_kcal_total_day_norm ~ qPCR_Cmin_SQ_per_gCC_norm*Sex + qPCR_Round + Promethion_weight_norm +(1|Batch/Sex/Promethion_Cabinet), data = metadata_Cmin_HL_qPCR_bc)

boundary (singular) fit: see help('isSingular')

lmerTest::step(model_bombcal_Cmin)

Warning: unable to evaluate scaled gradientWarning: Model failed to converge: degenerate  Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -1.4e-03boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                                     Eliminated npar  logLik    AIC    LRT Df Pr(>Chisq)  
<none>                                            10 -111.21 242.41                       
(1 | Sex:Batch)                               1    9 -111.21 240.41 0.0000  1    1.00000  
(1 | Batch)                                   2    8 -111.21 238.41 0.0000  1    1.00000  
(1 | Promethion_Cabinet:(Sex:Batch))          0    7 -114.14 242.27 5.8594  1    0.01549 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                              Eliminated  Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
qPCR_Cmin_SQ_per_gCC_norm:Sex          1  0.0497  0.0497     1 79.932  0.0826 0.7745795    
Sex                                    2  0.7347  0.7347     1 22.271  1.2380 0.2777203    
qPCR_Round                             3  0.7736  0.7736     1 79.658  1.3059 0.2565668    
qPCR_Cmin_SQ_per_gCC_norm              4  0.9449  0.9449     1 83.458  1.5865 0.2113406    
Promethion_weight_norm                 0 10.6658 10.6658     1 45.900 17.5674 0.0001249 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
bombcal_Calorific_Value_kcal_total_day_norm ~ Promethion_weight_norm + (1 | Promethion_Cabinet:(Sex:Batch))

model_bombcal_biomass <- lmerTest::lmer(bombcal_Calorific_Value_kcal_total_day_norm ~ qPCR_16S_SQ_per_gCC_norm*Sex + qPCR_Round + Promethion_weight_norm +(1|Batch/Sex/Promethion_Cabinet), data = metadata_Cmin_HL_qPCR_bc)

boundary (singular) fit: see help('isSingular')

lmerTest::step(model_bombcal_biomass)

boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')

Backward reduced random-effect table:

                                     Eliminated npar  logLik    AIC   LRT Df Pr(>Chisq)  
<none>                                            10 -111.78 243.57                      
(1 | Sex:Batch)                               1    9 -111.78 241.57 0.000  1    1.00000  
(1 | Batch)                                   2    8 -111.78 239.57 0.000  1    1.00000  
(1 | Promethion_Cabinet:(Sex:Batch))          0    7 -114.52 243.04 5.471  1    0.01933 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Backward reduced fixed-effect table:
Degrees of freedom method: Satterthwaite 

                             Eliminated  Sum Sq Mean Sq NumDF  DenDF F value    Pr(>F)    
qPCR_16S_SQ_per_gCC_norm:Sex          1  0.2379  0.2379     1 76.549  0.3863 0.5360894    
qPCR_16S_SQ_per_gCC_norm              2  0.0188  0.0188     1 77.540  0.0308 0.8612200    
Sex                                   3  0.8487  0.8487     1 24.232  1.4087 0.2467763    
qPCR_Round                            4  0.8994  0.8994     1 81.231  1.4937 0.2251849    
Promethion_weight_norm                0 10.6658 10.6658     1 45.900 17.5674 0.0001249 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Model found:
bombcal_Calorific_Value_kcal_total_day_norm ~ Promethion_weight_norm + (1 | Promethion_Cabinet:(Sex:Batch))

model_bombcal_Cmin.2 <- lmerTest::lmer(bombcal_Calorific_Value_kcal_total_day_norm ~ qPCR_Cmin_SQ_per_gCC_norm + Promethion_weight_norm + (1 | Promethion_Cabinet:(Sex:Batch)), data = metadata_Cmin_HL_qPCR_bc)
model_bombcal_biomass.2 <- lmerTest::lmer(bombcal_Calorific_Value_kcal_total_day_norm ~ qPCR_16S_SQ_per_gCC_norm + Promethion_weight_norm + (1 | Promethion_Cabinet:(Sex:Batch)), data = metadata_Cmin_HL_qPCR_bc)

shapiro.test(residuals(model_bombcal_Cmin.2))

    Shapiro-Wilk normality test

data:  residuals(model_bombcal_Cmin.2)
W = 0.99156, p-value = 0.8481

shapiro.test(residuals(model_bombcal_biomass.2))

    Shapiro-Wilk normality test

data:  residuals(model_bombcal_biomass.2)
W = 0.99057, p-value = 0.7836

anova(lm(residuals(model_bombcal_Cmin.2)~fitted.values(model_bombcal_Cmin.2)))

Analysis of Variance Table

Response: residuals(model_bombcal_Cmin.2)
                                    Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_bombcal_Cmin.2)  1  0.575 0.57455  1.0795 0.3017
Residuals                           86 45.772 0.53223               

anova(lm(residuals(model_bombcal_biomass.2)~fitted.values(model_bombcal_biomass.2)))

Analysis of Variance Table

Response: residuals(model_bombcal_biomass.2)
                                       Df Sum Sq Mean Sq F value Pr(>F)
fitted.values(model_bombcal_biomass.2)  1  0.574 0.57358  1.0376 0.3112
Residuals                              86 47.539 0.55278               

car::Anova(model_bombcal_Cmin.2, test.statistic ="F", type ="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: bombcal_Calorific_Value_kcal_total_day_norm
                                F Df Df.res    Pr(>F)    
qPCR_Cmin_SQ_per_gCC_norm  1.5364  1 83.437 0.2186307    
Promethion_weight_norm    14.2885  1 49.656 0.0004214 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

car::Anova(model_bombcal_biomass.2, test.statistic ="F", type ="II")

Analysis of Deviance Table (Type II Wald F tests with Kenward-Roger df)

Response: bombcal_Calorific_Value_kcal_total_day_norm
                              F Df Df.res    Pr(>F)    
qPCR_16S_SQ_per_gCC_norm  0.012  1 78.927 0.9131847    
Promethion_weight_norm   15.259  1 49.522 0.0002844 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

r2_model_bombcal_Cmin_mar<- partR2(model_bombcal_Cmin.2, partvars = c("qPCR_Cmin_SQ_per_gCC_norm"), 
                  R2_type = "marginal", nboot = 10)

  |                                                  | 0 % ~calculating  
  |+++++                                             | 10% ~01s          
  |++++++++++                                        | 20% ~01s          
  |+++++++++++++++                                   | 30% ~01s          
  |++++++++++++++++++++                              | 40% ~01s          
  |+++++++++++++++++++++++++                         | 50% ~01s          
  |++++++++++++++++++++++++++++++                    | 60% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  

r2_model_bombcal_Cmin_mar$R2 %>% dplyr::filter(term == "qPCR_Cmin_SQ_per_gCC_norm") %>% dplyr::select(estimate) %>% pull()

[1] 0.01108785

r2_model_bombcal_biomass_mar<- partR2(model_bombcal_biomass.2, partvars = c("qPCR_16S_SQ_per_gCC_norm"), 
                  R2_type = "marginal", nboot = 10)

  |                                                  | 0 % ~calculating  
  |+++++                                             | 10% ~01s          
  |++++++++++                                        | 20% ~01s          
  |+++++++++++++++                                   | 30% ~01s          
  |++++++++++++++++++++                              | 40% ~01s          
  |+++++++++++++++++++++++++                         | 50% ~01s          
  |++++++++++++++++++++++++++++++                    | 60% ~01s          
  |+++++++++++++++++++++++++++++++++++               | 70% ~00s          
  |++++++++++++++++++++++++++++++++++++++++          | 80% ~00s          
  |+++++++++++++++++++++++++++++++++++++++++++++     | 90% ~00s          
  |++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed=02s  

r2_model_bombcal_biomass_mar$R2 %>% dplyr::filter(term == "qPCR_16S_SQ_per_gCC_norm") %>% dplyr::select(estimate) %>% pull()

[1] 7.367549e-05

model_bombcal_HL_resid <- lmer(bombcal_Calorific_Value_kcal_total_day_norm ~ Sex + Promethion_weight_norm+ (1 | Promethion_Cabinet:(Sex:Batch)), data = metadata_Cmin_HL_qPCR_bc)
metadata_Cmin_HL_qPCR_bc$bombcal_Calorific_Value_kcal_total_day_resid <- residuals(model_bombcal_HL_resid)/sigma(model_bombcal_HL_resid)

FigS7_biomass_bc<- metadata_Cmin_HL_qPCR_bc %>%
  ggplot(aes(x= qPCR_16S_SQ_per_gCC_norm ,y=bombcal_Calorific_Value_kcal_total_day_resid)) +
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5, color ="black")+ 
  geom_point(aes(color = Cmin_load, shape = Sex), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_HL, label = c(expression(italic("Low-CM")), expression(italic("High-CM"))), name = "Group") +
  theme_classic(base_size = 10) +
  labs(x = "Norm. microbes", y = "Adj. fecal energy\ncontent") 
FigS7_biomass_bc

FigS7_cmin_bc<- metadata_Cmin_HL_qPCR_bc %>%
  ggplot(aes(x= qPCR_Cmin_SQ_per_gCC_norm ,y=bombcal_Calorific_Value_kcal_total_day_resid)) +
  geom_smooth(method = "lm", se = F,  alpha = 0.8, size = 0.5, color ="black")+ 
  geom_point(aes(color = Cmin_load, shape = Sex), alpha =0.7, size =0.75, show.legend = T) +
  scale_shape_manual(values=shape_Sex, label = c("Female", "Male"),name = "Sex") + 
  scale_color_manual(values=color_HL, label = c(expression(italic("Low-CM")), expression(italic("High-CM"))), name = "Group") +
  theme_classic(base_size = 10) +
  labs(x = "Norm. C. minuta", y = "Adj. fecal energy\ncontent")
FigS7_cmin_bc

alpha hL

Alpha_div_HL <- metadata_Cmin_HL %>%
  dplyr::select(MouseID, Cmin_load)  %>%
  dplyr::left_join(Alpha_div, by ="MouseID") %>% 
  tidyr::drop_na(Cmin_load, shannon_entropy)

Alpha_div_HL_M <- Alpha_div_HL %>% 
  filter(Sex =="M")
Alpha_div_HL_F <- Alpha_div_HL %>% 
  filter(Sex =="F")

wilcox.test(shannon_entropy~Cmin_load, data = Alpha_div_HL_M, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  shannon_entropy by Cmin_load
W = 1072, p-value = 0.501
alternative hypothesis: true location shift is not equal to 0

wilcox.test(shannon_entropy~Cmin_load, data = Alpha_div_HL_F, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  shannon_entropy by Cmin_load
W = 1036, p-value = 0.8636
alternative hypothesis: true location shift is not equal to 0

wilcox.test(pielou_evenness~Cmin_load, data = Alpha_div_HL_M, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  pielou_evenness by Cmin_load
W = 898, p-value = 0.4503
alternative hypothesis: true location shift is not equal to 0

wilcox.test(pielou_evenness~Cmin_load, data = Alpha_div_HL_F, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  pielou_evenness by Cmin_load
W = 820, p-value = 0.06309
alternative hypothesis: true location shift is not equal to 0

wilcox.test(faith_pd~Cmin_load, data = Alpha_div_HL_M, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  faith_pd by Cmin_load
W = 1322, p-value = 0.006441
alternative hypothesis: true location shift is not equal to 0

wilcox.test(faith_pd~Cmin_load, data = Alpha_div_HL_F, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  faith_pd by Cmin_load
W = 1424, p-value = 0.004262
alternative hypothesis: true location shift is not equal to 0

wilcox.test(observed_features~Cmin_load, data = Alpha_div_HL_M, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  observed_features by Cmin_load
W = 1330, p-value = 0.005269
alternative hypothesis: true location shift is not equal to 0

wilcox.test(observed_features~Cmin_load, data = Alpha_div_HL_F, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  observed_features by Cmin_load
W = 1393, p-value = 0.008895
alternative hypothesis: true location shift is not equal to 0

–> sex-dimorphic effects gone! –> analyses by group not separated by sex anymore!

wilcox.test(shannon_entropy~Cmin_load, data = Alpha_div_HL, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  shannon_entropy by Cmin_load
W = 4309, p-value = 0.5437
alternative hypothesis: true location shift is not equal to 0

wilcox.test(pielou_evenness~Cmin_load, data = Alpha_div_HL, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  pielou_evenness by Cmin_load
W = 3531, p-value = 0.1095
alternative hypothesis: true location shift is not equal to 0

wilcox.test(faith_pd~Cmin_load, data = Alpha_div_HL, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  faith_pd by Cmin_load
W = 5457, p-value = 0.0001113
alternative hypothesis: true location shift is not equal to 0

wilcox.test(observed_features~Cmin_load, data = Alpha_div_HL, exact = FALSE, correct = FALSE, conf.int = FALSE)

    Wilcoxon rank sum test

data:  observed_features by Cmin_load
W = 5433.5, p-value = 0.000146
alternative hypothesis: true location shift is not equal to 0

mean_se_HL <- Alpha_div_HL %>%
  dplyr::group_by(Cmin_load) %>%
  dplyr::summarise(mean = mean(shannon_entropy), se = standard_error(shannon_entropy))%>%
  dplyr::mutate(alpha = "se")

mean_pe_HL <- Alpha_div_HL %>%
  dplyr::group_by(Cmin_load) %>%
  dplyr::summarise(mean = mean(pielou_evenness), se = standard_error(pielou_evenness))%>%
  dplyr::mutate(alpha = "pe")

mean_fdp_HL <- Alpha_div_HL %>%
  dplyr::group_by(Cmin_load) %>%
  dplyr::summarise(mean = mean(faith_pd), se = standard_error(faith_pd))%>%
  dplyr::mutate(alpha = "fdp")

mean_of_HL <- Alpha_div_HL %>%
  dplyr::group_by(Cmin_load) %>%
  dplyr::summarise(mean = mean(observed_features), se = standard_error(observed_features))%>%
  dplyr::mutate(alpha = "of")

rbind(mean_se_HL, mean_pe_HL, mean_fdp_HL, mean_of_HL)

FigS7_alpha_HL_MR <- Alpha_div_HL %>% 
  drop_na(Cmin_load) %>% 
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels= c("Low", "High"))) %>% 
  ggplot(aes(x = Cmin_load, y = observed_features, shape = Sex, color = Cmin_load)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Cmin_load), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_HL, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Low" = "Low\nCM", "High" = "High\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "# microbial species") +
  theme(axis.text.x = element_text(face = "italic")) 
FigS7_alpha_HL_MR

FigS7_alpha_HL_SE <- Alpha_div_HL %>% 
  drop_na(Cmin_load) %>% 
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels= c("Low", "High"))) %>% 
  ggplot(aes(x = Cmin_load, y = shannon_entropy, shape = Sex, color = Cmin_load)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Cmin_load), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_HL, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Low" = "Low\nCM", "High" = "High\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Shannon Entropy")+
  theme(axis.text.x = element_text(face = "italic")) 
FigS7_alpha_HL_SE

FigS7_alpha_HL_FPD <- Alpha_div_HL %>% 
  drop_na(Cmin_load) %>% 
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels= c("Low", "High"))) %>% 
  ggplot(aes(x = Cmin_load, y = faith_pd, shape = Sex, color = Cmin_load)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Cmin_load), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_HL, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Low" = "Low\nCM", "High" = "High\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Faith's PD")+
  theme(axis.text.x = element_text(face = "italic")) 
FigS7_alpha_HL_FPD

FigS7_alpha_HL_PE <- Alpha_div_HL %>% 
  drop_na(Cmin_load) %>% 
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels= c("Low", "High"))) %>% 
  ggplot(aes(x = Cmin_load, y = pielou_evenness, shape = Sex, color = Cmin_load)) + 
  geom_quasirandom(alpha = 0.7, size = 0.75) +
  stat_summary(fun = mean, geom = "point", size=2, aes(group = Cmin_load), color = "black", alpha = 0.6, shape = 15) +
  scale_color_manual(values = color_HL, label = c("Killed-CM", "Live-CM"), name = "Treatment") +
  scale_shape_manual(values = shape_Sex, label = c("Female", "Male")) +
  scale_x_discrete(labels=c("Low" = "Low\nCM", "High" = "High\nCM")) +
  theme_classic(base_size = 10) +
  theme(axis.title.x=element_blank(), plot.title = element_text(hjust = 0.5)) +
  labs(y = "Pilou evenness")+
  theme(axis.text.x = element_text(face = "italic")) 
FigS7_alpha_HL_PE

beta diversity

reads_rare_norm_metadata <- reads_rare_norm_metadata[ order(row.names(reads_rare_norm_metadata)), ] 

metadata_Cmin_load <- metadata_Cmin_HL %>%
  dplyr::select(MouseID, Cmin_load)

reads_rare_norm_metadata.melt_HL <- reads_rare_norm_metadata %>% 
  tibble::rownames_to_column(var = "sampleid")  %>%
  dplyr::left_join(metadata_Cmin_load, by ="MouseID") %>%
  dplyr::select(sampleid, Sex, Cmin_load) %>%
  dplyr::mutate_if(is.factor,as.character)

wUF_melt_HL <- melt(as.matrix(Beta_wUF.2)) %>%
    filter(as.character(Var1) != as.character(Var2)) %>%
    mutate_if(is.factor,as.character)

uwUF_melt_HL <- melt(as.matrix(Beta_uwUF.2)) %>%
    filter(as.character(Var1) != as.character(Var2)) %>%
    mutate_if(is.factor,as.character)

colnames(reads_rare_norm_metadata.melt_HL) = c("Var1", "Sex1", "Cmin_load1")
wUF_melt_HL = left_join(wUF_melt_HL, reads_rare_norm_metadata.melt_HL, by = "Var1")
uwUF_melt_HL = left_join(uwUF_melt_HL, reads_rare_norm_metadata.melt_HL, by = "Var1")

colnames(reads_rare_norm_metadata.melt_HL) = c("Var2", "Sex2", "Cmin_load2")
wUF_melt_HL = left_join(wUF_melt_HL, reads_rare_norm_metadata.melt_HL, by = "Var2")
uwUF_melt_HL = left_join(uwUF_melt_HL, reads_rare_norm_metadata.melt_HL, by = "Var2")

set.seed(765)

wUF_melt.HL <- wUF_melt_HL %>%
  dplyr::filter(Cmin_load1 == Cmin_load2, Sex1 == Sex2) %>%
  dplyr::mutate(Cmin_load = Cmin_load1, Sex = Sex1)

wilcox.test(value ~ Cmin_load, data = wUF_melt.HL, exact = FALSE, correct = FALSE)

    Wilcoxon rank sum test

data:  value by Cmin_load
W = 6273800, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0

uwUF_melt.HL <- uwUF_melt_HL %>%
  dplyr::filter(Cmin_load1 == Cmin_load2, Sex1 == Sex2) %>%
  dplyr::mutate(Cmin_load = Cmin_load1, Sex = Sex1) 

wilcox.test(value ~ Cmin_load, data = uwUF_melt.HL, exact = FALSE, correct = FALSE) 

    Wilcoxon rank sum test

data:  value by Cmin_load
W = 6352464, p-value < 2.2e-16
alternative hypothesis: true location shift is not equal to 0

FigS7_wUF <- wUF_melt.HL %>%
  drop_na(Cmin_load) %>%
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels= c("Low", "High"))) %>% 
  ggplot(aes(x = Cmin_load, y = value, fill=factor(Cmin_load))) +
  theme_classic(base_size = 10) +
  geom_boxplot(alpha =0.7, outlier.size = 0.3, outlier.alpha = 0.5) +
  scale_fill_manual(values=color_HL)+
  scale_x_discrete(labels=c("Low" = "Low\nCM", "High" = "High\nCM")) +
  labs(y = "weighted\nUniFrac distances")  +
  theme(axis.text.x = element_text(face = "italic"), axis.title.x=element_blank()) 
FigS7_wUF

FigS7_uwUF <-uwUF_melt.HL %>%
  drop_na(Cmin_load) %>%
  dplyr::mutate(Cmin_load = factor(Cmin_load, levels= c("Low", "High"))) %>% 
  ggplot(aes(x = Cmin_load, y = value, fill=factor(Cmin_load))) +
  theme_classic(base_size = 10) +
  geom_boxplot(alpha =0.7, outlier.size = 0.3, outlier.alpha = 0.5) +
  scale_fill_manual(values=color_HL)+
  scale_x_discrete(labels=c("Low" = "Low\nCM", "High" = "High\nCM")) +
  labs(y = "unweighted\nUniFrac distances")  +
  theme(axis.text.x = element_text(face = "italic"), axis.title.x=element_blank())
FigS7_uwUF

FigS7_HL_legend <- cowplot::get_legend(metadata_Cmin_HL %>% 
  dplyr::mutate(Sex = ifelse(Sex == "F", "Female", "Male"), Cmin_load = ifelse(Cmin_load == "Low", "Low-CM", "High-CM")) %>%
  dplyr::mutate(Sex_Cmin_load = paste(Sex, Cmin_load, sep = " ")) %>%
  ggplot() +
  geom_point(aes(x = Sex_Cmin_load, y = qPCR_Cmin_per_gCC, shape = Sex_Cmin_load, color = Sex_Cmin_load), size =3 , alpha =1,
             show.legend = T) +
  scale_shape_manual(values=c(16, 16,4, 4)) + 
  scale_color_manual(values= c(color_HL, color_HL)) +
  theme_classic(base_size = 10) +
  theme(legend.title = element_blank()) + 
  theme(legend.position = "bottom"))

FigS7_align <- cowplot::align_plots(FigS7_mbiomass_HL + theme(legend.position="none"),
                                            FigS7_biomass_bc + theme(legend.position="none"),
                                            FigS7_cmin_bc + theme(legend.position="none"),
                                            FigS7_alpha_HL_MR + theme(legend.position="none"),
                                            FigS7_alpha_HL_SE + theme(legend.position="none"),
                                            FigS7_alpha_HL_FPD + theme(legend.position="none"),
                                            FigS7_alpha_HL_PE + theme(legend.position="none"),
                                            FigS7_wUF + theme(legend.position="none"),
                                            FigS7_uwUF + theme(legend.position="none"),
                                            align = 'hv', axis = 'tblr')

`geom_smooth()` using formula 'y ~ x'
`geom_smooth()` using formula 'y ~ x'

FigS7_1 <- cowplot::plot_grid(FigS7_align[[1]],
                                       FigS7_align[[2]], 
                                       FigS7_align[[3]], 
                                       FigS7_align[[4]],
                                       FigS7_align[[6]],
                                       FigS7_align[[5]],
                                       FigS7_align[[7]],
                                       FigS7_align[[8]],
                                       FigS7_align[[9]],
                                       ncol=3, nrow=3, labels = c("A", "B", "C", "D", "E", "F", "G", "H", "I"), label_size = 12, rel_widths = c(1,1, 1))

FigureS7 <- cowplot::plot_grid(FigS7_1,
                                       FigS7_HL_legend,
                                       ncol=1, nrow=2,  rel_heights = c(3, 0.05))

FigureS7

ggsave("./FigureS7.pdf", plot = FigureS7, device = cairo_pdf,  
  width = 16.5,
  height = 14,
  units = "cm")

sessionInfo()

R version 4.0.2 (2020-06-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS:   /opt/R/4.0.2/lib/R/lib/libRblas.so
LAPACK: /opt/R/4.0.2/lib/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8   
 [6] LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] ggbeeswarm_0.6.0           ggh4x_0.2.2                cowplot_1.1.1              rsq_2.5                    partR2_0.9.1.9000         
 [6] mapdata_2.3.1              maps_3.4.1                 ggmap_3.0.1                patchwork_1.1.2            vegan_2.5-7               
[11] permute_0.9-7              effects_4.2-1              Maaslin2_1.7.3             emmeans_1.7.2              reshape2_1.4.4            
[16] lmerTest_3.1-3             lme4_1.1-28                Matrix_1.2-18              PerformanceAnalytics_2.0.4 xts_0.12.1                
[21] zoo_1.8-9                  bestNormalize_1.8.2        Rmisc_1.5                  plyr_1.8.6                 lattice_0.20-41           
[26] car_3.0-12                 carData_3.0-5              lubridate_1.8.0            ggpubr_0.4.0               ggforce_0.3.3             
[31] scales_1.2.1               ggthemes_4.2.4             forcats_0.5.1              stringr_1.4.0              dplyr_1.0.8               
[36] purrr_0.3.4                readr_2.1.2                tidyr_1.2.0                tibble_3.1.6               ggplot2_3.3.5             
[41] tidyverse_1.3.1            data.table_1.14.2          switchr_0.14.5            

loaded via a namespace (and not attached):
  [1] readxl_1.3.1        backports_1.4.1     sp_1.4-6            splines_4.0.2       listenv_0.8.0       usethis_2.1.5      
  [7] lpsymphony_1.18.0   digest_0.6.29       butcher_0.1.5       foreach_1.5.2       fansi_1.0.2         magrittr_2.0.2     
 [13] cluster_2.1.0       doParallel_1.0.17   tzdb_0.2.0          remotes_2.4.2       recipes_0.2.0       globals_0.14.0     
 [19] modelr_0.1.8        gower_1.0.0         hardhat_0.2.0       jpeg_0.1-10         colorspace_2.0-3    rvest_1.0.2        
 [25] mitools_2.4         haven_2.4.3         xfun_0.29           crayon_1.5.0        RCurl_1.98-1.7      jsonlite_1.8.0     
 [31] biglm_0.9-2.1       survival_3.1-12     iterators_1.0.14    glue_1.6.1          polyclip_1.10-0     gtable_0.3.0       
 [37] ipred_0.9-12        future.apply_1.8.1  DEoptimR_1.0-10     abind_1.4-5         mvtnorm_1.1-3       DBI_1.1.2          
 [43] rngtools_1.5.2      rstatix_0.7.0       Rcpp_1.0.8          xtable_1.8-4        lava_1.6.10         survey_4.1-1       
 [49] prodlim_2019.11.13  getopt_1.20.3       httr_1.4.2          ellipsis_0.3.2      pkgconfig_2.0.3     farver_2.1.0       
 [55] nnet_7.3-14         dbplyr_2.1.1        utf8_1.2.2          RJSONIO_1.3-1.6     labeling_0.4.2      tidyselect_1.1.2   
 [61] rlang_1.0.1         munsell_0.5.0       cellranger_1.1.0    tools_4.0.2         cli_3.2.0           generics_0.1.2     
 [67] broom_0.7.12        knitr_1.37          fs_1.5.2            robustbase_0.93-9   RgoogleMaps_1.4.5.3 pbapply_1.5-0      
 [73] future_1.24.0       nlme_3.1-148        doRNG_1.8.2         xml2_1.3.3          pbkrtest_0.5.1      compiler_4.0.2     
 [79] rstudioapi_0.13     beeswarm_0.4.0      png_0.1-7           ggsignif_0.6.3      reprex_2.0.1        tweenr_1.0.2       
 [85] pcaPP_1.9-74        stringi_1.7.6       nloptr_1.2.2.3      vctrs_0.3.8         pillar_1.7.0        lifecycle_1.0.1    
 [91] optparse_1.7.1      estimability_1.3    bitops_1.0-7        insight_0.17.1      R6_2.5.1            vipor_0.4.5        
 [97] parallelly_1.30.0   codetools_0.2-16    boot_1.3-25         MASS_7.3-51.6       assertthat_0.2.1    nortest_1.0-4      
[103] withr_2.5.0         Deriv_4.1.3         mgcv_1.8-31         parallel_4.0.2      hms_1.1.1           quadprog_1.5-8     
[109] grid_4.0.2          rpart_4.1-15        timeDate_3043.102   coda_0.19-4         class_7.3-17        minqa_1.2.4        
[115] numDeriv_2016.8-1.1